323:). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records. If the answer is the time data field, then this is a time series data set candidate. If determining a unique record requires a time data field and an additional identifier which is unrelated to time (e.g. student ID, stock symbol, country code), then it is panel data candidate. If the differentiation lies on the non-time identifier, then the data set is a cross-sectional data set candidate.
433:. An example chart is shown on the right for tuberculosis incidence in the United States, made with a spreadsheet program. The number of cases was standardized to a rate per 100,000 and the percent change per year in this rate was calculated. The nearly steadily dropping line shows that the TB incidence was decreasing in most years, but the percent change in this rate varied by as much as +/- 10%, with 'surges' in 1975 and around the early 1990s. The use of both vertical axes allows the comparison of two time series in one graphic.
5894:
5842:
416:
40:
741:, but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of statistics is that it provides a means of transferring knowledge about a sample of a population to the whole population, and to other related populations, which is not necessarily the same as prediction over time. When information is transferred across time, often to specific points in time, the process is known as
5828:
969:. An additional set of extensions of these models is available for use where the observed time-series is driven by some "forcing" time-series (which may not have a causal effect on the observed series): the distinction from the multivariate case is that the forcing series may be deterministic or under the experimenter's control. For these models, the acronyms are extended with a final "X" for "exogenous".
534:
5866:
5854:
995:, TARCH, EGARCH, FIGARCH, CGARCH, etc.). Here changes in variability are related to, or predicted by, recent past values of the observed series. This is in contrast to other possible representations of locally varying variability, where the variability might be modelled as being driven by a separate time-varying process, as in a
880:
Splitting a time-series into a sequence of segments. It is often the case that a time-series can be represented as a sequence of individual segments, each with its own characteristic properties. For example, the audio signal from a conference call can be partitioned into pieces corresponding to the
548:
between values ("benchmarks") for earlier and later dates. Interpolation is estimation of an unknown quantity between two known quantities (historical data), or drawing conclusions about missing information from the available information ("reading between the lines"). Interpolation is useful where
1002:
In recent work on model-free analyses, wavelet transform based methods (for example locally stationary wavelets and wavelet decomposed neural networks) have gained favor. Multiscale (often referred to as multiresolution) techniques decompose a given time series, attempting to illustrate time
190:
model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be
517:
such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of a function where no data are available, and to summarize the relationships among two or more variables.
965:(ARFIMA) model generalizes the former three. Extensions of these classes to deal with vector-valued data are available under the heading of multivariate time-series models and sometimes the preceding acronyms are extended by including an initial "V" for "vector", as in VAR for
566:). The main difference between regression and interpolation is that polynomial regression gives a single polynomial that models the entire data set. Spline interpolation, however, yield a piecewise continuous function composed of many polynomials to model the data set.
174:
is often employed in such a way as to test relationships between one or more different time series, this type of analysis is not usually called "time series analysis", which refers in particular to relationships between different points in time within a single series.
1783:
Time series can be visualized with two categories of chart: Overlapping Charts and
Separated Charts. Overlapping Charts display all-time series on the same layout while Separated Charts presents them on different layouts (but aligned for comparison purpose)
881:
times during which each person was speaking. In time-series segmentation, the goal is to identify the segment boundary points in the time-series, and to characterize the dynamical properties associated with each segment. One can approach this problem using
182:, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from
916:
using chunking with sliding windows. It was found that the cluster centers (the average of the time series in a cluster - also a time series) follow an arbitrarily shifted sine pattern (regardless of the dataset, even on realizations of a
436:
A study of corporate data analysts found two challenges to exploratory time series analysis: discovering the shape of interesting patterns, and finding an explanation for these patterns. Visual tools that represent time series data as
561:
functions are fitted in time intervals such that they fit smoothly together. A different problem which is closely related to interpolation is the approximation of a complicated function by a simple function (also called
598:
among a well-defined class that closely matches ("approximates") a target function in a task-specific way. One can distinguish two major classes of function approximation problems: First, for known target functions,
549:
the data surrounding the missing data is available and its trend, seasonality, and longer-term cycles are known. This is often done by using a related series known for all relevant dates. Alternatively
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time series. However, more importantly, empirical investigations can indicate the advantage of using predictions derived from non-linear models, over those from linear models, as for example in
5924:
694:
time series approximation is to summarize the data in one-pass and construct an approximate representation that can support a variety of time series queries with bounds on worst-case error.
464:. For example, sunspot activity varies over 11 year cycles. Other common examples include celestial phenomena, weather patterns, neural activity, commodity prices, and economic activity.
1014:(HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be considered as the simplest
962:
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Simple or fully formed statistical models to describe the likely outcome of the time series in the immediate future, given knowledge of the most recent outcomes (forecasting).
186:
where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A
541:
For processes that are expected to generally grow in magnitude one of the curves in the graphic at right (and many others) can be fitted by estimating their parameters.
2695:
Yoe, Charles E. (March 1996). An
Introduction to Risk and Uncertainty in the Evaluation of Environmental Investments (Report). U.S. Army Corps of Engineers. p. 69.
572:
is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to
1141:. Both models and applications can be developed under each of these conditions, although the models in the latter case might be considered as only partly specified.
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988:
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Time series data may be clustered, however special care has to be taken when considering subsequence clustering. Time series clustering may be split into
1966:
Lin, Jessica; Keogh, Eamonn; Lonardi, Stefano; Chiu, Bill (2003). "A symbolic representation of time series, with implications for streaming algorithms".
3883:
1749:
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NikoliÄ, Danko; MureĆan, Raul C.; Feng, Weijia; Singer, Wolf (March 2012). "Scaled correlation analysis: a better way to compute a cross-correlogram".
1527:
2765:
Gandhi, Sorabh; Foschini, Luca; Suri, Subhash (2010). "Space-efficient online approximation of time series data: Streams, amnesia, and out-of-order".
2552:
Daud, Hanita; Sagayan, Vijanth; Yahya, Noorhana; Najwati, Wan (2009). "Modeling of
Electromagnetic Waves Using Statistical and Numerical Techniques".
921:). This means that the found cluster centers are non-descriptive for the dataset because the cluster centers are always nonrepresentative sine waves.
5016:
977:
958:
5455:
2885:
Keogh, Eamonn; Lin, Jessica (August 2005). "Clustering of time-series subsequences is meaningless: implications for previous and future research".
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purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future
3233:
Ropella, G.E.P.; Nag, D.A.; Hunt, C.A. (2003). "Similarity measures for automated comparison of in silico and in vitro experimental results".
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Discrete, continuous or mixed spectra of time series, depending on whether the time series contains a (generalized) harmonic signal or not
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Proceedings of the 25th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)
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Non-linear dependence of the level of a series on previous data points is of interest, partly because of the possibility of producing a
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Machine
Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
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2112:
1993:
1412:
2095:
Keogh, Eamonn; Kasetty, Shruti (2002). "On the need for time series data mining benchmarks: A survey and empirical demonstration".
319:. Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a
3449:
155:
comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
5934:
5443:
5317:
331:
There are several types of motivation and data analysis available for time series which are appropriate for different purposes.
276:). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast,
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Forecasting on time series is usually done using automated statistical software packages and programming languages, such as
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4754:
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1322:
1216:
1004:
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4804:
3000:"Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide"
1698:
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5139:
5011:
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4759:
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2060:
Aghabozorgi, Saeed; Seyed
Shirkhorshidi, Ali; Ying Wah, Teh (October 2015). "Time-series clustering â A decade review".
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Ergodicity implies stationarity, but the converse is not necessarily the case. Stationarity is usually classified into
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Assigning time series pattern to a specific category, for example identify a word based on series of hand movements in
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Among other types of non-linear time series models, there are models to represent the changes of variance over time (
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Separation into components representing trend, seasonality, slow and fast variation, and cyclical irregularity: see
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or non-stationary. Situations where the amplitudes of frequency components change with time can be dealt with in
864:
376:
277:
257:
5420:
3119:
Sakoe, H.; Chiba, S. (February 1978). "Dynamic programming algorithm optimization for spoken word recognition".
1693:
1037:
A number of different notations are in use for time-series analysis. A common notation specifying a time series
5790:
5557:
5105:
5070:
5034:
4819:
4261:
4170:
4129:
4041:
3732:
3571:
3483:. Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis (Santa Fe, May 1992),
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Agrawal, Rakesh; Faloutsos, Christos; Swami, Arun (1993). "Efficient similarity search in sequence databases".
1757:
1332:
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768:
619:) that often have desirable properties (inexpensive computation, continuity, integral and limit values, etc.).
550:
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2520:
Fitting Models to
Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting
166:
to predict future values based on previously observed values. Generally, time series data is modelled as a
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1971:
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933:. When modeling variations in the level of a process, three broad classes of practical importance are the
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119:
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1507:
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and others for filtering signals from noise and predicting signal values at a certain point in time. See
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has a certain structure which can be described using a small number of parameters (for example, using an
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5275:
5001:
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115:
28:
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885:, or by modeling the time-series as a more sophisticated system, such as a Markov jump linear system.
544:
The construction of economic time series involves the estimation of some components for some dates by
530:
since it may reflect the method used to construct the curve as much as it reflects the observed data.
5914:
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348:
320:
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273:
107:
3317:
3212:
Chevyrev, Ilya; Kormilitzin, Andrey (2016). "A Primer on the
Signature Method in Machine Learning".
2443:
2097:
Proceedings of the eighth ACM SIGKDD international conference on
Knowledge discovery and data mining
1968:
Proceedings of the 8th ACM SIGMOD workshop on
Research issues in data mining and knowledge discovery
626:, may be unknown; instead of an explicit formula, only a set of points (a time series) of the form (
509:, in which a "smooth" function is constructed that approximately fits the data. A related topic is
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300:
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111:
99:
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178:
Time series data have a natural temporal ordering. This makes time series analysis distinct from
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1999:
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analysis. In the time domain, correlation and analysis can be made in a filter-like manner using
230:
192:
167:
3550:
3198:
209:
data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the
980:. Further references on nonlinear time series analysis: (Kantz and Schreiber), and (Abarbanel)
900:
subsequence time series clustering (single timeseries, split into chunks using sliding windows)
576:, which produces estimates between known observations, but extrapolation is subject to greater
5752:
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2423:
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2212:
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1989:
1930:
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2100:
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1981:
1941:
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39:
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448:
135:
2917:
3276:"The TimeViz Browser:A Visual Survey of Visualization Techniques for Time-Oriented Data"
3015:
2738:
Friedman, Milton (December 1962). "The Interpolation of Time Series by Related Series".
2188:
2030:
191:
expressed as deriving in some way from past values, rather than from future values (see
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5638:
4101:
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64:
43:
Time series: random data plus trend, with best-fit line and different applied filters
3953:
3334:
3260:
3154:
Mormann, Florian; Andrzejak, Ralph G.; Elger, Christian E.; Lehnertz, Klaus (2007).
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2003:
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103:
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59:
indexed (or listed or graphed) in time order. Most commonly, a time series is a
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functions are fulfilled if we have a good to moderate fit for the observed data.
1910:
1880:
1562:
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1256:
1186:
cross- and auto-correlation functions to remove contributions of slow components
918:
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527:
461:
404:
380:
364:
356:
226:
199:
139:
48:
3242:
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897:
whole time series clustering (multiple time series for which to find a cluster)
533:
288:
of the process without assuming that the process has any particular structure.
4631:
4111:
3811:
3742:
3692:
3667:
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3023:
2898:
2814:
2774:
2542:
Regression Analysis By Rudolf J. Freund, William J. Wilson, Ping Sa. Page 269.
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91:
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56:
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TomĂĄs, R.; Li, Z.; Lopez-Sanchez, J. M.; Liu, P.; Singleton, A. (June 2016).
2073:
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3948:
3943:
3908:
3509:
3498:
2506:
2236:
2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
2139:
2017:
Warren Liao, T. (November 2005). "Clustering of time series dataâa survey".
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83:
63:
taken at successive equally spaced points in time. Thus it is a sequence of
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3094:
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2216:
17:
2852:
2230:
Sarkar, Advait; Spott, Martin; Blackwell, Alan F.; Jamnik, Mateja (2016).
2104:
1985:
1118:
There are two sets of conditions under which much of the theory is built:
912:
Subsequence time series clustering resulted in unstable (random) clusters
501:
points, possibly subject to constraints. Curve fitting can involve either
429:
A straightforward way to examine a regular time series is manually with a
4300:
3918:
3795:
3790:
3785:
3757:
3481:
Time Series Prediction: Forecasting the Future and Understanding the Past
1809:
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285:
72:
60:
3054:
Time-Frequency Signal Analysis and Processing: A Comprehensive Reference
929:
Models for time series data can have many forms and represent different
5805:
5506:
3031:
2767:
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
2556:. Lecture Notes in Computer Science. Vol. 5857. pp. 686â695.
2232:"Visual discovery and model-driven explanation of time series patterns"
3551:
Introduction to Time series Analysis (Engineering Statistics Handbook)
2197:
1144:
In addition, time-series analysis can be applied where the series are
991:(ARCH) and the collection comprises a wide variety of representation (
5727:
4708:
4682:
4662:
3913:
3704:
3495:
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
611:) can be approximated by a specific class of functions (for example,
2614:. Methods in Experimental Physics. Vol. 13. pp. 115â346 .
2134:. Lecture Notes in Computer Science. Vol. 730. pp. 69â84.
3218:
2834:
Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah (2014).
3467:
Time Series Analysis and its Applications: With R Examples (ed. 4)
1850:
1304:
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414:
252:
Additionally, time series analysis techniques may be divided into
249:, thereby mitigating the need to operate in the frequency domain.
221:
Methods for time series analysis may be divided into two classes:
68:
38:
594:
In general, a function approximation problem asks us to select a
3647:
2610:
William, Dudley, ed. (1976). "Nuclear and Atomic Spectroscopy".
839:, the development of which was significantly accelerated during
776:
498:
143:
5616:
5183:
4930:
4229:
3999:
3616:
3560:
3556:
3275:
3121:
IEEE Transactions on Acoustics, Speech, and Signal Processing
953:
on previous data points. Combinations of these ideas produce
607:
that investigates how certain known functions (for example,
1022:, for translating a time series of spoken words into text.
379:
it is used for signal detection. Other applications are in
3504:
Woodward, W. A., Gray, H. L. & Elliott, A. C. (2012),
2173:"Ordinal Time Series Forecasting of the Air Quality Index"
1025:
Many of these models are collected in the python package
291:
Methods of time series analysis may also be divided into
737:. One particular approach to such inference is known as
460:
to examine cyclic behavior which need not be related to
3347:
Time Series Analysis: forecasting and control, rev. ed.
2589:. Springer Science & Business Media. p. 227.
2379:. Springer Science & Business Media. p. 165.
5882:
1007:(MSMF) techniques for modeling volatility evolution.
963:
autoregressive fractionally integrated moving-average
5469:
Autoregressive conditional heteroskedasticity (ARCH)
580:
and a higher risk of producing meaningless results.
5776:
5713:
5666:
5629:
5584:
5566:
5533:
5524:
5482:
5429:
5390:
5339:
5330:
5251:
5208:
5138:
5104:
5058:
5025:
4987:
4954:
4866:
4775:
4694:
4649:
4617:
4570:
4515:
4441:
4432:
4242:
4184:
4158:
4110:
4065:
4012:
3899:
3854:
3828:
3810:
3766:
3718:
3638:
3629:
2171:Chen, Cathy W. S.; Chiu, L. M. (4 September 2021).
638:)) is provided. Depending on the structure of the
67:data. Examples of time series are heights of ocean
2674:. Rowman & Littlefield Publishers. p. 1.
2587:Numerical Methods for Nonlinear Engineering Models
2554:Visual Informatics: Bridging Research and Practice
2442:Numerical Methods in Engineering with MATLABÂź. By
1164:Tools for investigating time-series data include:
786:using the Spark-TS library, a third-party package.
5925:Mathematical and quantitative methods (economics)
2923:. New York: Cambridge University Press. pp.
782:Forecasting on large scale data can be done with
505:, where an exact fit to the data is required, or
3479:Weigend A. S., Gershenfeld N. A. (Eds.) (1994),
2836:"A Review of Subsequence Time Series Clustering"
2518:Motulsky, Harvey; Christopoulos, Arthur (2004).
2277:Fourier Analysis of Time Series: An Introduction
29:Time (disambiguation) § Film and television
5017:Multivariate adaptive regression splines (MARS)
3303:(2006). "25 Tears of Time Series Forecasting".
3156:"Seizure prediction: the long and winding road"
2740:Journal of the American Statistical Association
2131:Foundations of Data Organization and Algorithms
1613:Loss of recurrence (degree of non-stationarity)
522:refers to the use of a fitted curve beyond the
489:Curve fitting is the process of constructing a
82:A time series is very frequently plotted via a
2715:Numerical Methods for Scientists and Engineers
2458:Numerical Methods in Engineering with Python 3
2406:: Why So Many Predictions Fail-but Some Don't.
1745:Pearson product-moment correlation coefficient
1610:Surrogate time series and surrogate correction
3572:
3199:"Measuring the 'Complexity' of a time series"
3114:
3112:
989:autoregressive conditional heteroskedasticity
391:, where time series analysis can be used for
8:
3553:â A practical guide to Time series analysis.
2811:(slides of a talk at Spark Summit East 2016)
363:the primary goal of time series analysis is
3358:Time Series Analysis by State Space Methods
2485:. Cambridge University Press. p. 349.
5626:
5613:
5530:
5336:
5205:
5180:
4951:
4927:
4655:
4438:
4239:
4226:
4009:
3996:
3635:
3626:
3613:
3579:
3565:
3557:
2672:Community Analysis and Planning Techniques
2460:. Cambridge University Press. p. 21.
2377:Advanced Techniques of Population Analysis
2352:Curve Fitting for Programmable Calculators
526:of the observed data, and is subject to a
3446:High Performance Discovery in Time Series
3316:
3217:
3173:
2948:Kantz, Holger; Thomas, Schreiber (2004).
2861:
2851:
2206:
2196:
1975:
978:nonlinear autoregressive exogenous models
2302:Applied Statistical Time Series Analysis
1003:dependence at multiple scales. See also
959:autoregressive integrated moving-average
949:(MA) models. These three classes depend
697:To some extent, the different problems (
5889:
3274:Tominski, Christian; Aigner, Wolfgang.
1958:
1750:Spearman's rank correlation coefficient
709:) have received a unified treatment in
686:is a finite set, one is dealing with a
650:, several techniques for approximating
497:, that has the best fit to a series of
198:Time series analysis can be applied to
134:, and largely in any domain of applied
5543:KaplanâMeier estimator (product limit)
2952:. London: Cambridge University Press.
1970:. New York: ACM Press. pp. 2â11.
1741:Data interpreted as stochastic series
1703:Data as vectors in a metrizable space
690:problem instead. A related problem of
3465:Shumway R. H., Stoffer D. S. (2017),
1180:and cross-spectral density functions)
622:Second, the target function, call it
513:, which focuses more on questions of
75:, and the daily closing value of the
7:
5853:
5553:Accelerated failure time (AFT) model
3307:. Twenty Five Years of Forecasting.
3305:International Journal of Forecasting
1828:Reduced line chart (small multiples)
749:Fully formed statistical models for
654:may be applicable. For example, if
441:can help overcome these challenges.
5865:
5148:Analysis of variance (ANOVA, anova)
3370:The Nature of Mathematical Modeling
2919:The Nature of Mathematical Modeling
2327:Practical Handbook of Curve Fitting
1718:Data as time series with envelopes
1338:Time-frequency analysis techniques:
419:Tuberculosis incidence US 1953â2009
5243:CochranâMantelâHaenszel statistics
3869:Pearson product-moment correlation
3522:(1st ed.). Packt Publishing.
2483:Numerical Methods of Curve Fitting
1385:Recurrence quantification analysis
1300:Shewhart individuals control chart
908:Subsequence time series clustering
25:
3427:Spectral Analysis and Time Series
3349:, Oakland, California: Holden-Day
3056:, Elsevier Science, Oxford, 2003
2975:Analysis of Observed Chaotic Data
2973:Abarbanel, Henry (Nov 25, 1997).
2887:Knowledge and Information Systems
2808:"Time Series Analysis with Spark"
2375:Halli, S. S.; Rao, K. V. (1992).
1411:that can be used for time series
1193:to investigate the series in the
914:induced by the feature extraction
36:Sequence of data points over time
5892:
5864:
5852:
5840:
5827:
5826:
3327:10.1016/j.ijforecast.2006.01.001
3087:10.1111/j.1460-9568.2011.07987.x
3075:European Journal of Neuroscience
2420:Data Preparation for Data Mining
1323:Nonlinear mixed-effects modeling
1018:. HMM models are widely used in
827:and filtering of signals in the
5502:Least-squares spectral analysis
2670:Klosterman, Richard E. (1990).
2646:Encyclopedia of Research Design
1896:Least-squares spectral analysis
1645:Dynamical Entrainment (physics)
4483:Mean-unbiased minimum-variance
2950:Nonlinear Time Series Analysis
2752:10.1080/01621459.1962.10500812
1861:Detrended fluctuation analysis
1540:Fluctuation dispersion entropy
1481:Univariate non-linear measures
1318:Detrended fluctuation analysis
1:
5796:Geographic information system
5012:Simultaneous equations models
2620:10.1016/S0076-695X(08)60643-2
2481:Guest, Philip George (2012).
1638:Bivariate non-linear measures
1631:Coherence (signal processing)
1217:empirical orthogonal function
1154:timeâfrequency representation
1005:Markov switching multifractal
955:autoregressive moving-average
315:A time series is one type of
266:stationary stochastic process
4979:Coefficient of determination
4590:Uniformly most powerful test
3506:Applied Time Series Analysis
3197:Land, Bruce; Elias, Damian.
2840:The Scientific World Journal
2562:10.1007/978-3-642-05036-7_65
2039:10.1016/j.patcog.2005.01.025
1856:Decomposition of time series
1699:PraisâWinsten transformation
1363:Fractional Fourier transform
1353:Short-time Fourier transform
1348:Continuous wavelet transform
1282:Multi expression programming
1236:Unobserved components models
1213:Principal component analysis
1156:of a time-series or signal.
473:decomposition of time series
229:methods. The former include
77:Dow Jones Industrial Average
5548:Proportional hazards models
5492:Spectral density estimation
5474:Vector autoregression (VAR)
4908:Maximum posterior estimator
4140:Randomized controlled trial
3052:Boashash, B. (ed.), (2003)
2522:. Oxford University Press.
2325:Arlinghaus, Sandra (1994).
2300:Shumway, Robert H. (1988).
1947:Unevenly spaced time series
1272:Gene expression programming
1178:cross-correlation functions
1073:Another common notation is
837:spectral density estimation
713:, where they are viewed as
711:statistical learning theory
264:assume that the underlying
90:). Time series are used in
5951:
5308:Multivariate distributions
3728:Average absolute deviation
3407:Princeton University Press
3374:Cambridge University Press
3368:Gershenfeld, Neil (2000),
3345:; Jenkins, Gwilym (1976),
3243:10.1109/IEMBS.2003.1280532
3133:10.1109/TASSP.1978.1163055
2275:Bloomfield, Peter (1976).
2244:10.1109/vlhcc.2016.7739668
1769:CramĂ©râvon Mises criterion
1642:Non-linear interdependence
1445:energy (signal processing)
1424:Univariate linear measures
1247:Artificial neural networks
1223:Singular spectrum analysis
987:). These models represent
873:
823:This approach is based on
812:
797:Statistical classification
794:
721:Prediction and forecasting
587:
482:
444:Other techniques include:
422:
132:communications engineering
26:
5822:
5625:
5612:
5296:Structural equation model
5204:
5179:
4950:
4926:
4658:
4632:Score/Lagrange multiplier
4238:
4225:
4047:Sample size determination
4008:
3995:
3625:
3612:
3594:
3024:10.1007/s10346-015-0589-y
2899:10.1007/s10115-004-0172-7
2806:Sandy Ryza (2020-03-18).
2775:10.1109/ICDE.2010.5447930
2713:Hamming, Richard (2012).
2643:Salkind, Neil J. (2010).
2350:Kolb, William M. (1984).
1866:Digital signal processing
1834:Circular silhouette graph
1619:Bivariate linear measures
1584:Other univariate measures
1174:spectral density function
1139:second-order stationarity
865:Digital signal processing
682:(range or target set) of
377:communication engineering
278:non-parametric approaches
5791:Environmental statistics
5313:Elliptical distributions
5106:Generalized linear model
5035:Simple linear regression
4805:HodgesâLehmann estimator
4262:Probability distribution
4171:Stochastic approximation
3733:Coefficient of variation
2585:Hauser, John R. (2009).
2456:Kiusalaas, Jaan (2013).
2404:The Signal and the Noise
2074:10.1016/j.is.2015.04.007
1758:probability distribution
1333:Dynamic Bayesian network
1170:autocorrelation function
1016:dynamic Bayesian network
876:Time-series segmentation
557:is used where piecewise
551:polynomial interpolation
280:explicitly estimate the
5935:Mathematics in medicine
5451:Cross-correlation (XCF)
5059:Non-standard predictors
4493:LehmannâScheffĂ© theorem
4166:Adaptive clinical trial
3362:Oxford University Press
3356:, Koopman S.J. (2001),
2717:. Courier Corporation.
2140:10.1007/3-540-57301-1_5
1764:KolmogorovâSmirnov test
1552:Marginal predictability
1449:Characteristics of the
1439:Spectral edge frequency
1407:Time-series metrics or
1150:time-frequency analysis
1041:that is indexed by the
997:doubly stochastic model
847:, electrical engineers
658:is an operation on the
321:cross-sectional dataset
180:cross-sectional studies
5920:Statistical data types
5847:Mathematics portal
5668:Engineering statistics
5576:NelsonâAalen estimator
5153:Analysis of covariance
5040:Ordinary least squares
4964:Pearson product-moment
4368:Statistical functional
4279:Empirical distribution
4112:Controlled experiments
3841:Frequency distribution
3619:Descriptive statistics
3516:Auffarth, Ben (2021).
3237:. pp. 2933â2936.
2977:. New York: Springer.
1756:Data interpreted as a
1589:Algorithmic complexity
1565:dissimilarity measures
1485:Measures based on the
1343:Fast Fourier transform
1252:Support vector machine
883:change-point detection
590:Function approximation
584:Function approximation
538:
420:
120:electroencephalography
44:
5763:Population statistics
5705:System identification
5439:Autocorrelation (ACF)
5367:Exponential smoothing
5281:Discriminant analysis
5276:Canonical correlation
5140:Partition of variance
5002:Regression validation
4846:(JonckheereâTerpstra)
4745:Likelihood-ratio test
4434:Frequentist inference
4346:Locationâscale family
4267:Sampling distribution
4232:Statistical inference
4199:Cross-sectional study
4186:Observational studies
4145:Randomized experiment
3974:Stem-and-leaf display
3776:Central limit theorem
2649:. SAGE. p. 266.
2418:Pyle, Dorian (1999).
2105:10.1145/775047.775062
1986:10.1145/882082.882086
1650:phase synchronization
1594:Kolmogorov complexity
1493:Correlation dimension
1375:Correlation dimension
1227:"Structural" models:
1168:Consideration of the
1152:which makes use of a
1146:seasonally stationary
967:vector autoregression
903:time point clustering
751:stochastic simulation
735:statistical inference
707:fitness approximation
678:can be used. If the
536:
528:degree of uncertainty
515:statistical inference
495:mathematical function
423:Further information:
418:
262:parametric approaches
237:; the latter include
184:spatial data analysis
116:earthquake prediction
86:(which is a temporal
42:
5686:Probabilistic design
5271:Principal components
5114:Exponential families
5066:Nonlinear regression
5045:General linear model
5007:Mixed effects models
4997:Errors and residuals
4974:Confounding variable
4876:Bayesian probability
4854:Van der Waerden test
4844:Ordered alternative
4609:Multiple comparisons
4488:RaoâBlackwellization
4451:Estimating equations
4407:Statistical distance
4125:Factorial experiment
3658:Arithmetic-Geometric
3403:Time Series Analysis
3299:De Gooijer, Jan G.;
3175:10.1093/brain/awl241
2769:. pp. 924â935.
2099:. pp. 102â111.
1936:Time series database
1712:Mahalanobis distance
1694:NeweyâWest estimator
1674:Dynamic time warping
1606:Rough path signature
1557:Dynamical similarity
1548:Higher-order methods
1498:Correlation integral
1468:Autoregressive model
1429:Moment (mathematics)
1328:Dynamic time warping
961:(ARIMA) models. The
943:(I) models, and the
931:stochastic processes
739:predictive inference
601:approximation theory
555:spline interpolation
451:analysis to examine
425:Exploratory analysis
411:Exploratory analysis
399:, query by content,
367:. In the context of
349:quantitative finance
274:moving-average model
217:Methods for analysis
108:mathematical finance
5758:Official statistics
5681:Methods engineering
5362:Seasonal adjustment
5130:Poisson regressions
5050:Bayesian regression
4989:Regression analysis
4969:Partial correlation
4941:Regression analysis
4540:Prediction interval
4535:Likelihood interval
4525:Confidence interval
4517:Interval estimation
4478:Unbiased estimators
4296:Model specification
4176:Up-and-down designs
3864:Partial correlation
3820:Index of dispersion
3738:Interquartile range
3444:Shasha, D. (2004),
3016:2016Lands..13..437T
2853:10.1155/2014/312521
2422:. Morgan Kaufmann.
2189:2021Entrp..23.1167C
2062:Information Systems
2031:2005PatRe..38.1857W
2019:Pattern Recognition
1921:Seasonal adjustment
1846:Anomaly time series
1679:Hidden Markov model
1663:Similarity measures
1600:Hidden Markov model
1573:Permutation methods
1513:Approximate entropy
1508:Correlation entropy
1503:Correlation density
1434:Spectral band power
1417:regression analysis
1277:Hidden Markov model
1267:Genetic programming
1206:to remove unwanted
1135:strict stationarity
1012:hidden Markov model
715:supervised learning
672:regression analysis
511:regression analysis
385:pattern recognition
373:control engineering
202:, continuous data,
172:regression analysis
124:control engineering
112:weather forecasting
100:pattern recognition
27:For TV series, see
5778:Spatial statistics
5658:Medical statistics
5558:First hitting time
5512:Whittle likelihood
5163:Degrees of freedom
5158:Multivariate ANOVA
5091:Heteroscedasticity
4903:Bayesian estimator
4868:Bayesian inference
4717:KolmogorovâSmirnov
4602:Randomization test
4572:Testing hypotheses
4545:Tolerance interval
4456:Maximum likelihood
4351:Exponential family
4284:Density estimation
4244:Statistical theory
4204:Natural experiment
4150:Scientific control
4067:Survey methodology
3753:Standard deviation
2238:. pp. 78â86.
1916:Scaled correlation
1901:Monte Carlo method
1886:Frequency spectrum
1788:Overlapping charts
1735:standard deviation
1729:standard deviation
1723:standard deviation
1707:Minkowski distance
1537:Dispersion entropy
1390:Lyapunov exponents
1358:Chirplet transform
1232:state space models
1137:and wide-sense or
1123:Stationary process
1020:speech recognition
985:heteroskedasticity
617:rational functions
605:numerical analysis
539:
421:
339:In the context of
247:scaled correlation
193:time reversibility
168:stochastic process
45:
5880:
5879:
5818:
5817:
5814:
5813:
5753:National accounts
5723:Actuarial science
5715:Social statistics
5608:
5607:
5604:
5603:
5600:
5599:
5535:Survival function
5520:
5519:
5382:Granger causality
5223:Contingency table
5198:Survival analysis
5175:
5174:
5171:
5170:
5027:Linear regression
4922:
4921:
4918:
4917:
4893:Credible interval
4862:
4861:
4645:
4644:
4461:Method of moments
4330:Parametric family
4291:Statistical model
4221:
4220:
4217:
4216:
4135:Random assignment
4057:Statistical power
3991:
3990:
3987:
3986:
3836:Contingency table
3806:
3805:
3673:Generalized/power
3475:978-3-319-52451-1
3459:978-0-387-00857-8
3439:978-0-12-564901-8
3416:978-0-691-04289-3
3383:978-0-521-57095-4
3252:978-0-7803-7789-9
2784:978-1-4244-5445-7
2724:978-0-486-13482-6
2681:978-0-7425-7440-3
2656:978-1-4129-6127-1
2629:978-0-12-475913-8
2596:978-1-4020-9920-5
2571:978-3-642-05035-0
2529:978-0-19-803834-4
2492:978-1-107-64695-7
2467:978-1-139-62058-1
2429:978-1-55860-529-9
2386:978-0-306-43997-1
2361:978-0-943494-02-9
2336:978-0-8493-0143-8
2311:978-0-13-041500-4
2304:. Prentice-Hall.
2286:978-0-471-08256-9
2253:978-1-5090-0252-8
2198:10.3390/e23091167
2149:978-3-540-57301-2
2025:(11): 1857â1874.
1931:Signal processing
1926:Sequence analysis
1876:Estimation theory
1689:Total correlation
1669:Cross-correlation
1625:cross-correlation
1569:Lyapunov exponent
1474:MannâKendall test
1457:Hjorth parameters
1191:Fourier transform
939:(AR) models, the
861:Estimation theory
843:by mathematician
833:Fourier transform
825:harmonic analysis
819:Estimation theory
815:Signal processing
809:Signal estimation
609:special functions
603:is the branch of
458:Spectral analysis
453:serial dependence
439:heat map matrices
401:anomaly detection
369:signal processing
243:cross-correlation
231:spectral analysis
96:signal processing
16:(Redirected from
5942:
5930:Machine learning
5897:
5896:
5888:
5868:
5867:
5856:
5855:
5845:
5844:
5830:
5829:
5733:Crime statistics
5627:
5614:
5531:
5497:Fourier analysis
5484:Frequency domain
5464:
5411:
5377:Structural break
5337:
5286:Cluster analysis
5233:Log-linear model
5206:
5181:
5122:
5096:Homoscedasticity
4952:
4928:
4847:
4839:
4831:
4830:(KruskalâWallis)
4815:
4800:
4755:Cross validation
4740:
4722:AndersonâDarling
4669:
4656:
4627:Likelihood-ratio
4619:Parametric tests
4597:Permutation test
4580:1- & 2-tails
4471:Minimum distance
4443:Point estimation
4439:
4390:Optimal decision
4341:
4240:
4227:
4209:Quasi-experiment
4159:Adaptive designs
4010:
3997:
3874:Rank correlation
3636:
3627:
3614:
3581:
3574:
3567:
3558:
3540:
3538:
3536:
3462:
3423:Priestley, M. B.
3419:
3394:
3350:
3338:
3320:
3287:
3286:
3284:
3282:
3271:
3265:
3264:
3230:
3224:
3223:
3221:
3209:
3203:
3202:
3194:
3188:
3187:
3177:
3151:
3145:
3144:
3116:
3107:
3106:
3070:
3064:
3050:
3044:
3043:
2995:
2989:
2988:
2970:
2964:
2963:
2945:
2939:
2938:
2922:
2909:
2903:
2902:
2882:
2876:
2875:
2865:
2855:
2831:
2825:
2824:
2822:
2821:
2812:
2803:
2797:
2796:
2762:
2756:
2755:
2746:(300): 729â757.
2735:
2729:
2728:
2710:
2704:
2703:
2692:
2686:
2685:
2667:
2661:
2660:
2640:
2634:
2633:
2607:
2601:
2600:
2582:
2576:
2575:
2549:
2543:
2540:
2534:
2533:
2515:
2509:
2503:
2497:
2496:
2478:
2472:
2471:
2453:
2447:
2440:
2434:
2433:
2415:
2409:
2400:
2394:
2393:
2372:
2366:
2365:
2347:
2341:
2340:
2322:
2316:
2315:
2297:
2291:
2290:
2272:
2266:
2265:
2227:
2221:
2220:
2210:
2200:
2168:
2162:
2161:
2125:
2119:
2118:
2092:
2086:
2085:
2057:
2051:
2050:
2014:
2008:
2007:
1979:
1963:
1942:Trend estimation
1831:Silhouette graph
1818:Separated charts
1813:
1531:
1395:Entropy encoding
1380:Recurrence plots
1370:Chaotic analysis
1262:Gaussian process
1242:Machine learning
1195:frequency domain
1097:
1068:
849:Rudolf E. KĂĄlmĂĄn
829:frequency domain
779:and many others.
662:, techniques of
537:Growth equations
469:trend estimation
389:machine learning
239:auto-correlation
235:wavelet analysis
223:frequency-domain
211:English language
162:is the use of a
21:
5950:
5949:
5945:
5944:
5943:
5941:
5940:
5939:
5905:
5904:
5903:
5891:
5883:
5881:
5876:
5839:
5810:
5772:
5709:
5695:quality control
5662:
5644:Clinical trials
5621:
5596:
5580:
5568:Hazard function
5562:
5516:
5478:
5462:
5425:
5421:BreuschâGodfrey
5409:
5386:
5326:
5301:Factor analysis
5247:
5228:Graphical model
5200:
5167:
5134:
5120:
5100:
5054:
5021:
4983:
4946:
4945:
4914:
4858:
4845:
4837:
4829:
4813:
4798:
4777:Rank statistics
4771:
4750:Model selection
4738:
4696:Goodness of fit
4690:
4667:
4641:
4613:
4566:
4511:
4500:Median unbiased
4428:
4339:
4272:Order statistic
4234:
4213:
4180:
4154:
4106:
4061:
4004:
4002:Data collection
3983:
3895:
3850:
3824:
3802:
3762:
3714:
3631:Continuous data
3621:
3608:
3590:
3585:
3547:
3534:
3532:
3530:
3515:
3460:
3443:
3417:
3399:Hamilton, James
3397:
3384:
3367:
3341:
3318:10.1.1.154.9227
3301:Hyndman, Rob J.
3298:
3295:
3293:Further reading
3290:
3280:
3278:
3273:
3272:
3268:
3253:
3232:
3231:
3227:
3211:
3210:
3206:
3196:
3195:
3191:
3153:
3152:
3148:
3118:
3117:
3110:
3072:
3071:
3067:
3051:
3047:
2997:
2996:
2992:
2985:
2972:
2971:
2967:
2960:
2947:
2946:
2942:
2935:
2913:Gershenfeld, N.
2911:
2910:
2906:
2884:
2883:
2879:
2833:
2832:
2828:
2819:
2817:
2810:
2805:
2804:
2800:
2785:
2764:
2763:
2759:
2737:
2736:
2732:
2725:
2712:
2711:
2707:
2694:
2693:
2689:
2682:
2669:
2668:
2664:
2657:
2642:
2641:
2637:
2630:
2609:
2608:
2604:
2597:
2584:
2583:
2579:
2572:
2551:
2550:
2546:
2541:
2537:
2530:
2517:
2516:
2512:
2504:
2500:
2493:
2480:
2479:
2475:
2468:
2455:
2454:
2450:
2441:
2437:
2430:
2417:
2416:
2412:
2401:
2397:
2387:
2374:
2373:
2369:
2362:
2349:
2348:
2344:
2337:
2324:
2323:
2319:
2312:
2299:
2298:
2294:
2287:
2274:
2273:
2269:
2254:
2229:
2228:
2224:
2170:
2169:
2165:
2150:
2127:
2126:
2122:
2115:
2094:
2093:
2089:
2059:
2058:
2054:
2016:
2015:
2011:
1996:
1965:
1964:
1960:
1956:
1951:
1871:Distributed lag
1841:
1820:
1807:
1790:
1781:
1623:Maximum linear
1534:Wavelet entropy
1525:
1523:Fourier entropy
1451:autocorrelation
1405:
1289:Queueing theory
1162:
1128:Ergodic process
1116:
1086:
1077:
1066:
1059:
1049:
1043:natural numbers
1035:
927:
910:
891:
878:
872:
821:
811:
799:
793:
723:
592:
586:
487:
481:
449:Autocorrelation
427:
413:
337:
329:
313:
219:
142:which involves
55:is a series of
37:
32:
23:
22:
15:
12:
11:
5:
5948:
5946:
5938:
5937:
5932:
5927:
5922:
5917:
5907:
5906:
5902:
5901:
5878:
5877:
5875:
5874:
5862:
5850:
5836:
5823:
5820:
5819:
5816:
5815:
5812:
5811:
5809:
5808:
5803:
5798:
5793:
5788:
5782:
5780:
5774:
5773:
5771:
5770:
5765:
5760:
5755:
5750:
5745:
5740:
5735:
5730:
5725:
5719:
5717:
5711:
5710:
5708:
5707:
5702:
5697:
5688:
5683:
5678:
5672:
5670:
5664:
5663:
5661:
5660:
5655:
5650:
5641:
5639:Bioinformatics
5635:
5633:
5623:
5622:
5617:
5610:
5609:
5606:
5605:
5602:
5601:
5598:
5597:
5595:
5594:
5588:
5586:
5582:
5581:
5579:
5578:
5572:
5570:
5564:
5563:
5561:
5560:
5555:
5550:
5545:
5539:
5537:
5528:
5522:
5521:
5518:
5517:
5515:
5514:
5509:
5504:
5499:
5494:
5488:
5486:
5480:
5479:
5477:
5476:
5471:
5466:
5458:
5453:
5448:
5447:
5446:
5444:partial (PACF)
5435:
5433:
5427:
5426:
5424:
5423:
5418:
5413:
5405:
5400:
5394:
5392:
5391:Specific tests
5388:
5387:
5385:
5384:
5379:
5374:
5369:
5364:
5359:
5354:
5349:
5343:
5341:
5334:
5328:
5327:
5325:
5324:
5323:
5322:
5321:
5320:
5305:
5304:
5303:
5293:
5291:Classification
5288:
5283:
5278:
5273:
5268:
5263:
5257:
5255:
5249:
5248:
5246:
5245:
5240:
5238:McNemar's test
5235:
5230:
5225:
5220:
5214:
5212:
5202:
5201:
5184:
5177:
5176:
5173:
5172:
5169:
5168:
5166:
5165:
5160:
5155:
5150:
5144:
5142:
5136:
5135:
5133:
5132:
5116:
5110:
5108:
5102:
5101:
5099:
5098:
5093:
5088:
5083:
5078:
5076:Semiparametric
5073:
5068:
5062:
5060:
5056:
5055:
5053:
5052:
5047:
5042:
5037:
5031:
5029:
5023:
5022:
5020:
5019:
5014:
5009:
5004:
4999:
4993:
4991:
4985:
4984:
4982:
4981:
4976:
4971:
4966:
4960:
4958:
4948:
4947:
4944:
4943:
4938:
4932:
4931:
4924:
4923:
4920:
4919:
4916:
4915:
4913:
4912:
4911:
4910:
4900:
4895:
4890:
4889:
4888:
4883:
4872:
4870:
4864:
4863:
4860:
4859:
4857:
4856:
4851:
4850:
4849:
4841:
4833:
4817:
4814:(MannâWhitney)
4809:
4808:
4807:
4794:
4793:
4792:
4781:
4779:
4773:
4772:
4770:
4769:
4768:
4767:
4762:
4757:
4747:
4742:
4739:(ShapiroâWilk)
4734:
4729:
4724:
4719:
4714:
4706:
4700:
4698:
4692:
4691:
4689:
4688:
4680:
4671:
4659:
4653:
4651:Specific tests
4647:
4646:
4643:
4642:
4640:
4639:
4634:
4629:
4623:
4621:
4615:
4614:
4612:
4611:
4606:
4605:
4604:
4594:
4593:
4592:
4582:
4576:
4574:
4568:
4567:
4565:
4564:
4563:
4562:
4557:
4547:
4542:
4537:
4532:
4527:
4521:
4519:
4513:
4512:
4510:
4509:
4504:
4503:
4502:
4497:
4496:
4495:
4490:
4475:
4474:
4473:
4468:
4463:
4458:
4447:
4445:
4436:
4430:
4429:
4427:
4426:
4421:
4416:
4415:
4414:
4404:
4399:
4398:
4397:
4387:
4386:
4385:
4380:
4375:
4365:
4360:
4355:
4354:
4353:
4348:
4343:
4327:
4326:
4325:
4320:
4315:
4305:
4304:
4303:
4298:
4288:
4287:
4286:
4276:
4275:
4274:
4264:
4259:
4254:
4248:
4246:
4236:
4235:
4230:
4223:
4222:
4219:
4218:
4215:
4214:
4212:
4211:
4206:
4201:
4196:
4190:
4188:
4182:
4181:
4179:
4178:
4173:
4168:
4162:
4160:
4156:
4155:
4153:
4152:
4147:
4142:
4137:
4132:
4127:
4122:
4116:
4114:
4108:
4107:
4105:
4104:
4102:Standard error
4099:
4094:
4089:
4088:
4087:
4082:
4071:
4069:
4063:
4062:
4060:
4059:
4054:
4049:
4044:
4039:
4034:
4032:Optimal design
4029:
4024:
4018:
4016:
4006:
4005:
4000:
3993:
3992:
3989:
3988:
3985:
3984:
3982:
3981:
3976:
3971:
3966:
3961:
3956:
3951:
3946:
3941:
3936:
3931:
3926:
3921:
3916:
3911:
3905:
3903:
3897:
3896:
3894:
3893:
3888:
3887:
3886:
3881:
3871:
3866:
3860:
3858:
3852:
3851:
3849:
3848:
3843:
3838:
3832:
3830:
3829:Summary tables
3826:
3825:
3823:
3822:
3816:
3814:
3808:
3807:
3804:
3803:
3801:
3800:
3799:
3798:
3793:
3788:
3778:
3772:
3770:
3764:
3763:
3761:
3760:
3755:
3750:
3745:
3740:
3735:
3730:
3724:
3722:
3716:
3715:
3713:
3712:
3707:
3702:
3701:
3700:
3695:
3690:
3685:
3680:
3675:
3670:
3665:
3663:Contraharmonic
3660:
3655:
3644:
3642:
3633:
3623:
3622:
3617:
3610:
3609:
3607:
3606:
3601:
3595:
3592:
3591:
3586:
3584:
3583:
3576:
3569:
3561:
3555:
3554:
3546:
3545:External links
3543:
3542:
3541:
3529:978-1801819626
3528:
3513:
3502:
3488:
3485:Addison-Wesley
3477:
3463:
3458:
3441:
3431:Academic Press
3420:
3415:
3395:
3382:
3365:
3351:
3339:
3311:(3): 443â473.
3294:
3291:
3289:
3288:
3266:
3251:
3225:
3204:
3189:
3168:(2): 314â333.
3146:
3108:
3081:(5): 742â762.
3065:
3045:
3010:(3): 437â450.
2990:
2984:978-0387983721
2983:
2965:
2959:978-0521529020
2958:
2940:
2934:978-0521570954
2933:
2904:
2893:(2): 154â177.
2877:
2826:
2798:
2783:
2757:
2730:
2723:
2705:
2687:
2680:
2662:
2655:
2635:
2628:
2602:
2595:
2577:
2570:
2544:
2535:
2528:
2510:
2498:
2491:
2473:
2466:
2448:
2444:Jaan Kiusalaas
2435:
2428:
2410:
2408:By Nate Silver
2395:
2385:
2367:
2360:
2342:
2335:
2317:
2310:
2292:
2285:
2267:
2252:
2222:
2163:
2148:
2120:
2113:
2087:
2052:
2009:
1994:
1977:10.1.1.14.5597
1957:
1955:
1952:
1950:
1949:
1944:
1939:
1933:
1928:
1923:
1918:
1913:
1908:
1906:Panel analysis
1903:
1898:
1893:
1891:Hurst exponent
1888:
1883:
1878:
1873:
1868:
1863:
1858:
1853:
1848:
1842:
1840:
1837:
1836:
1835:
1832:
1829:
1826:
1824:Horizon graphs
1819:
1816:
1815:
1814:
1802:
1799:
1796:
1794:Braided graphs
1789:
1786:
1780:
1777:
1776:
1775:
1774:
1773:
1772:
1771:
1766:
1754:
1753:
1752:
1747:
1739:
1738:
1737:
1731:
1725:
1716:
1715:
1714:
1709:
1701:
1696:
1691:
1686:
1681:
1676:
1671:
1660:
1659:
1658:
1652:
1646:
1643:
1635:
1634:
1633:
1627:
1616:
1615:
1614:
1611:
1608:
1603:
1597:
1591:
1581:
1580:
1579:
1574:
1571:
1566:
1560:
1554:
1549:
1546:
1541:
1538:
1535:
1532:
1520:
1518:Sample entropy
1515:
1510:
1505:
1500:
1495:
1490:
1478:
1477:
1476:
1471:
1465:
1459:
1454:
1447:
1441:
1436:
1431:
1413:classification
1404:
1401:
1400:
1399:
1398:
1397:
1392:
1387:
1382:
1377:
1367:
1366:
1365:
1360:
1355:
1350:
1345:
1335:
1330:
1325:
1320:
1315:
1314:
1313:
1308:
1302:
1292:
1286:
1285:
1284:
1279:
1274:
1269:
1264:
1259:
1254:
1249:
1239:
1238:
1237:
1234:
1225:
1220:
1210:
1200:
1197:
1187:
1181:
1161:
1158:
1131:
1130:
1125:
1115:
1112:
1100:
1099:
1084:
1071:
1070:
1064:
1057:
1034:
1031:
946:moving-average
936:autoregressive
926:
923:
909:
906:
905:
904:
901:
898:
890:
887:
874:Main article:
871:
868:
845:Norbert Wiener
810:
807:
795:Main article:
792:
791:Classification
789:
788:
787:
780:
757:
754:
722:
719:
703:classification
688:classification
588:Main article:
585:
582:
483:Main article:
480:
477:
476:
475:
465:
455:
412:
409:
397:classification
336:
333:
328:
325:
312:
309:
270:autoregressive
258:non-parametric
218:
215:
146:measurements.
35:
24:
14:
13:
10:
9:
6:
4:
3:
2:
5947:
5936:
5933:
5931:
5928:
5926:
5923:
5921:
5918:
5916:
5913:
5912:
5910:
5900:
5895:
5890:
5886:
5873:
5872:
5863:
5861:
5860:
5851:
5849:
5848:
5843:
5837:
5835:
5834:
5825:
5824:
5821:
5807:
5804:
5802:
5801:Geostatistics
5799:
5797:
5794:
5792:
5789:
5787:
5784:
5783:
5781:
5779:
5775:
5769:
5768:Psychometrics
5766:
5764:
5761:
5759:
5756:
5754:
5751:
5749:
5746:
5744:
5741:
5739:
5736:
5734:
5731:
5729:
5726:
5724:
5721:
5720:
5718:
5716:
5712:
5706:
5703:
5701:
5698:
5696:
5692:
5689:
5687:
5684:
5682:
5679:
5677:
5674:
5673:
5671:
5669:
5665:
5659:
5656:
5654:
5651:
5649:
5645:
5642:
5640:
5637:
5636:
5634:
5632:
5631:Biostatistics
5628:
5624:
5620:
5615:
5611:
5593:
5592:Log-rank test
5590:
5589:
5587:
5583:
5577:
5574:
5573:
5571:
5569:
5565:
5559:
5556:
5554:
5551:
5549:
5546:
5544:
5541:
5540:
5538:
5536:
5532:
5529:
5527:
5523:
5513:
5510:
5508:
5505:
5503:
5500:
5498:
5495:
5493:
5490:
5489:
5487:
5485:
5481:
5475:
5472:
5470:
5467:
5465:
5463:(BoxâJenkins)
5459:
5457:
5454:
5452:
5449:
5445:
5442:
5441:
5440:
5437:
5436:
5434:
5432:
5428:
5422:
5419:
5417:
5416:DurbinâWatson
5414:
5412:
5406:
5404:
5401:
5399:
5398:DickeyâFuller
5396:
5395:
5393:
5389:
5383:
5380:
5378:
5375:
5373:
5372:Cointegration
5370:
5368:
5365:
5363:
5360:
5358:
5355:
5353:
5350:
5348:
5347:Decomposition
5345:
5344:
5342:
5338:
5335:
5333:
5329:
5319:
5316:
5315:
5314:
5311:
5310:
5309:
5306:
5302:
5299:
5298:
5297:
5294:
5292:
5289:
5287:
5284:
5282:
5279:
5277:
5274:
5272:
5269:
5267:
5264:
5262:
5259:
5258:
5256:
5254:
5250:
5244:
5241:
5239:
5236:
5234:
5231:
5229:
5226:
5224:
5221:
5219:
5218:Cohen's kappa
5216:
5215:
5213:
5211:
5207:
5203:
5199:
5195:
5191:
5187:
5182:
5178:
5164:
5161:
5159:
5156:
5154:
5151:
5149:
5146:
5145:
5143:
5141:
5137:
5131:
5127:
5123:
5117:
5115:
5112:
5111:
5109:
5107:
5103:
5097:
5094:
5092:
5089:
5087:
5084:
5082:
5079:
5077:
5074:
5072:
5071:Nonparametric
5069:
5067:
5064:
5063:
5061:
5057:
5051:
5048:
5046:
5043:
5041:
5038:
5036:
5033:
5032:
5030:
5028:
5024:
5018:
5015:
5013:
5010:
5008:
5005:
5003:
5000:
4998:
4995:
4994:
4992:
4990:
4986:
4980:
4977:
4975:
4972:
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4967:
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4962:
4961:
4959:
4957:
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4929:
4925:
4909:
4906:
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4887:
4884:
4882:
4879:
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4869:
4865:
4855:
4852:
4848:
4842:
4840:
4834:
4832:
4826:
4825:
4824:
4821:
4820:Nonparametric
4818:
4816:
4810:
4806:
4803:
4802:
4801:
4795:
4791:
4790:Sample median
4788:
4787:
4786:
4783:
4782:
4780:
4778:
4774:
4766:
4763:
4761:
4758:
4756:
4753:
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4711:
4707:
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4702:
4701:
4699:
4697:
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4687:
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4681:
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4422:
4420:
4417:
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4409:
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4403:
4400:
4396:
4395:loss function
4393:
4392:
4391:
4388:
4384:
4381:
4379:
4376:
4374:
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4369:
4366:
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4098:
4097:Questionnaire
4095:
4093:
4090:
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4078:
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3994:
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3950:
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3924:Control chart
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3109:
3104:
3100:
3096:
3092:
3088:
3084:
3080:
3076:
3069:
3066:
3063:
3062:0-08-044335-4
3059:
3055:
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3021:
3017:
3013:
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2329:. CRC Press.
2328:
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2114:1-58113-567-X
2110:
2106:
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2098:
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2067:
2063:
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2044:
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2032:
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2024:
2020:
2013:
2010:
2005:
2001:
1997:
1995:9781450374224
1991:
1987:
1983:
1978:
1973:
1969:
1962:
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1953:
1948:
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1779:Visualization
1778:
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1700:
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1684:Edit distance
1682:
1680:
1677:
1675:
1672:
1670:
1667:
1666:
1664:
1661:
1657:
1656:phase locking
1654:Measures for
1653:
1651:
1648:Measures for
1647:
1644:
1641:
1640:
1639:
1636:
1632:
1628:
1626:
1622:
1621:
1620:
1617:
1612:
1609:
1607:
1604:
1601:
1598:
1595:
1592:
1590:
1587:
1586:
1585:
1582:
1578:
1575:
1572:
1570:
1567:
1564:
1561:
1558:
1555:
1553:
1550:
1547:
1545:
1544:RĂ©nyi entropy
1542:
1539:
1536:
1533:
1529:
1524:
1521:
1519:
1516:
1514:
1511:
1509:
1506:
1504:
1501:
1499:
1496:
1494:
1491:
1488:
1484:
1483:
1482:
1479:
1475:
1472:
1469:
1466:
1463:
1460:
1458:
1455:
1452:
1448:
1446:
1442:
1440:
1437:
1435:
1432:
1430:
1427:
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1422:
1421:
1420:
1418:
1414:
1410:
1402:
1396:
1393:
1391:
1388:
1386:
1383:
1381:
1378:
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1373:
1372:
1371:
1368:
1364:
1361:
1359:
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1354:
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1341:
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1329:
1326:
1324:
1321:
1319:
1316:
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1309:
1306:
1303:
1301:
1298:
1297:
1296:
1295:Control chart
1293:
1290:
1287:
1283:
1280:
1278:
1275:
1273:
1270:
1268:
1265:
1263:
1260:
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1226:
1224:
1221:
1218:
1214:
1211:
1209:
1205:
1201:
1198:
1196:
1192:
1189:Performing a
1188:
1185:
1182:
1179:
1175:
1171:
1167:
1166:
1165:
1159:
1157:
1155:
1151:
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1121:
1120:
1119:
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1111:
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1091:
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1080:
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1074:
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1052:
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1047:
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1040:
1032:
1030:
1028:
1023:
1021:
1017:
1013:
1008:
1006:
1000:
998:
994:
990:
986:
981:
979:
975:
970:
968:
964:
960:
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952:
948:
947:
942:
938:
937:
932:
924:
922:
920:
915:
907:
902:
899:
896:
895:
894:
888:
886:
884:
877:
869:
867:
866:
862:
858:
857:Kalman filter
854:
850:
846:
842:
838:
834:
830:
826:
820:
816:
808:
806:
804:
803:sign language
798:
790:
785:
781:
778:
774:
770:
766:
762:
758:
755:
752:
748:
747:
746:
744:
740:
736:
733:is a part of
732:
728:
720:
718:
716:
712:
708:
704:
700:
695:
693:
689:
685:
681:
677:
676:curve fitting
673:
669:
668:extrapolation
665:
664:interpolation
661:
657:
653:
649:
645:
641:
637:
633:
629:
625:
620:
618:
614:
610:
606:
602:
597:
591:
583:
581:
579:
575:
574:interpolation
571:
570:Extrapolation
567:
565:
560:
556:
552:
547:
546:interpolation
542:
535:
531:
529:
525:
521:
520:Extrapolation
516:
512:
508:
504:
503:interpolation
500:
496:
492:
486:
485:Curve fitting
479:Curve fitting
478:
474:
470:
466:
463:
459:
456:
454:
450:
447:
446:
445:
442:
440:
434:
432:
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410:
408:
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334:
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326:
324:
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318:
310:
308:
306:
302:
298:
294:
289:
287:
283:
279:
275:
271:
267:
263:
260:methods. The
259:
255:
250:
248:
244:
240:
236:
232:
228:
224:
216:
214:
212:
208:
205:
201:
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176:
173:
169:
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154:
153:
147:
145:
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137:
133:
129:
125:
121:
117:
113:
109:
105:
101:
97:
93:
89:
85:
80:
78:
74:
70:
66:
65:discrete-time
62:
58:
54:
50:
41:
34:
30:
19:
5869:
5857:
5838:
5831:
5743:Econometrics
5693: /
5676:Chemometrics
5653:Epidemiology
5646: /
5619:Applications
5461:ARIMA model
5408:Q-statistic
5357:Stationarity
5331:
5253:Multivariate
5196: /
5193:
5192: /
5190:Multivariate
5188: /
5128: /
5124: /
4898:Bayes factor
4797:Signed rank
4709:
4683:
4675:
4663:
4358:Completeness
4194:Cohort study
4092:Opinion poll
4027:Missing data
4014:Study design
3969:Scatter plot
3891:Scatter plot
3884:Spearman's Ï
3846:Grouped data
3533:. Retrieved
3518:
3505:
3494:
3480:
3469:, Springer,
3466:
3445:
3426:
3402:
3369:
3357:
3346:
3308:
3304:
3279:. Retrieved
3269:
3234:
3228:
3207:
3192:
3165:
3159:
3149:
3127:(1): 43â49.
3124:
3120:
3078:
3074:
3068:
3053:
3048:
3007:
3003:
2993:
2974:
2968:
2949:
2943:
2918:
2907:
2890:
2886:
2880:
2843:
2839:
2829:
2818:. Retrieved
2801:
2766:
2760:
2743:
2739:
2733:
2714:
2708:
2690:
2671:
2665:
2645:
2638:
2612:Spectroscopy
2611:
2605:
2586:
2580:
2553:
2547:
2538:
2519:
2513:
2501:
2482:
2476:
2457:
2451:
2438:
2419:
2413:
2402:
2398:
2390:
2376:
2370:
2351:
2345:
2326:
2320:
2301:
2295:
2276:
2270:
2235:
2225:
2180:
2176:
2166:
2130:
2123:
2096:
2090:
2065:
2061:
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2022:
2018:
2012:
1967:
1961:
1801:Slope graphs
1782:
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1583:
1480:
1443:Accumulated
1423:
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1163:
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879:
870:Segmentation
853:Dennis Gabor
841:World War II
822:
800:
784:Apache Spark
724:
696:
691:
683:
660:real numbers
655:
651:
647:
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631:
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345:econometrics
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305:multivariate
290:
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225:methods and
220:
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157:Time series
156:
151:
150:Time series
149:
148:
104:econometrics
81:
71:, counts of
52:
46:
33:
5915:Time series
5899:Mathematics
5871:WikiProject
5786:Cartography
5748:Jurimetrics
5700:Reliability
5431:Time domain
5410:(LjungâBox)
5332:Time-series
5210:Categorical
5194:Time-series
5186:Categorical
5121:(Bernoulli)
4956:Correlation
4936:Correlation
4732:JarqueâBera
4704:Chi-squared
4466:M-estimator
4419:Asymptotics
4363:Sufficiency
4130:Interaction
4042:Replication
4022:Effect size
3979:Violin plot
3959:Radar chart
3939:Forest plot
3929:Correlogram
3879:Kendall's Ï
3343:Box, George
3032:10045/62160
2183:(9): 1167.
1911:Random walk
1881:Forecasting
1808: [
1798:Line charts
1563:State space
1526: [
1487:correlation
1257:Fuzzy logic
1045:is written
957:(ARMA) and
919:random walk
743:forecasting
613:polynomials
578:uncertainty
462:seasonality
405:forecasting
403:as well as
381:data mining
365:forecasting
357:meteorology
227:time-domain
200:real-valued
159:forecasting
140:engineering
57:data points
53:time series
49:mathematics
18:Time-series
5909:Categories
5738:Demography
5456:ARMA model
5261:Regression
4838:(Friedman)
4799:(Wilcoxon)
4737:Normality
4727:Lilliefors
4674:Student's
4550:Resampling
4424:Robustness
4412:divergence
4402:Efficiency
4340:(monotone)
4335:Likelihood
4252:Population
4085:Stratified
4037:Population
3856:Dependence
3812:Count data
3743:Percentile
3720:Dispersion
3653:Arithmetic
3588:Statistics
3535:5 November
3491:Wiener, N.
3219:1603.03788
3004:Landslides
2846:: 312521.
2820:2021-01-12
2815:Databricks
2505:See also:
2446:. Page 24.
2354:. SYNTEC.
1954:References
1577:Local flow
1470:parameters
1464:parameters
1311:EWMA chart
1114:Conditions
941:integrated
889:Clustering
831:using the
813:See also:
731:prediction
727:statistics
717:problems.
699:regression
564:regression
559:polynomial
431:line chart
393:clustering
361:geophysics
353:seismology
341:statistics
335:Motivation
317:panel data
311:Panel data
301:univariate
297:non-linear
282:covariance
254:parametric
188:stochastic
92:statistics
88:line chart
5119:Logistic
4886:posterior
4812:Rank sum
4560:Jackknife
4555:Bootstrap
4373:Bootstrap
4308:Parameter
4257:Statistic
4052:Statistic
3964:Run chart
3949:Pie chart
3944:Histogram
3934:Fan chart
3909:Bar chart
3791:L-moments
3678:Geometric
3510:CRC Press
3499:MIT Press
3392:174825352
3354:Durbin J.
3313:CiteSeerX
3040:1612-510X
2700:ADA316839
2507:Mollifier
2279:. Wiley.
2068:: 16â38.
1972:CiteSeerX
1760:function
1733:Windowed
1596:estimates
1219:analysis)
1202:Use of a
1108:index set
507:smoothing
128:astronomy
84:run chart
5833:Category
5526:Survival
5403:Johansen
5126:Binomial
5081:Isotonic
4668:(normal)
4313:location
4120:Blocking
4075:Sampling
3954:QâQ plot
3919:Box plot
3901:Graphics
3796:Skewness
3786:Kurtosis
3758:Variance
3688:Heronian
3683:Harmonic
3493:(1949),
3450:Springer
3425:(1981),
3401:(1994),
3335:14996235
3261:17798157
3184:17008335
3141:17900407
3095:22324876
2915:(1999).
2872:25140332
2793:16072352
2217:34573792
2158:16748451
1839:See also
1805:GapChart
1453:function
1409:features
1403:Measures
1291:analysis
1230:General
1172:and the
1033:Notation
951:linearly
680:codomain
644:codomain
596:function
327:Analysis
286:spectrum
204:discrete
170:. While
152:analysis
144:temporal
73:sunspots
61:sequence
5859:Commons
5806:Kriging
5691:Process
5648:studies
5507:Wavelet
5340:General
4507:Plug-in
4301:L space
4080:Cluster
3781:Moments
3599:Outline
3103:4694570
3012:Bibcode
2863:4130317
2262:9787931
2208:8469594
2185:Bibcode
2177:Entropy
2047:8973749
2027:Bibcode
2004:6084733
1721:Global
1629:Linear
1106:is the
974:chaotic
284:or the
207:numeric
136:science
5885:Portal
5728:Census
5318:Normal
5266:Manova
5086:Robust
4836:2-way
4828:1-way
4666:-test
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3914:Biplot
3705:Median
3698:Lehmer
3640:Center
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2080:
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1992:
1974:
1938:(TSDB)
1727:Local
1602:states
1204:filter
1184:Scaled
1176:(also
1102:where
1067:, ...)
1027:sktime
925:Models
863:, and
835:, and
765:Python
692:online
674:, and
640:domain
359:, and
299:, and
293:linear
5352:Trend
4881:prior
4823:anova
4712:-test
4686:-test
4678:-test
4585:Power
4530:Pivot
4323:shape
4318:scale
3768:Shape
3748:Range
3693:Heinz
3668:Cubic
3604:Index
3331:S2CID
3257:S2CID
3214:arXiv
3161:Brain
3137:S2CID
3099:S2CID
2789:S2CID
2258:S2CID
2154:S2CID
2078:S2CID
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2000:S2CID
1851:Chirp
1812:]
1559:index
1530:]
1307:chart
1305:CUSUM
1208:noise
1160:Tools
993:GARCH
761:Julia
524:range
493:, or
491:curve
164:model
69:tides
5585:Test
4785:Sign
4637:Wald
3710:Mode
3648:Mean
3537:2021
3524:ISBN
3471:ISBN
3454:ISBN
3435:ISBN
3411:ISBN
3388:OCLC
3378:ISBN
3283:2014
3247:ISBN
3180:PMID
3091:PMID
3058:ISBN
3036:ISSN
2979:ISBN
2954:ISBN
2929:ISBN
2868:PMID
2844:2014
2779:ISBN
2719:ISBN
2697:DTIC
2676:ISBN
2651:ISBN
2624:ISBN
2591:ISBN
2566:ISBN
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2462:ISBN
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