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are normally distributed, it is susceptible to the impact of rare events such as rapid shocks or anomalies. In contrast, the Moving Median, which is found by sorting the values inside the time window and finding the value in the middle, is more resistant to the impact of such rare events. This is because, for a given variance, the
Laplace distribution, which the Moving Median assumes, places higher probability on rare events than the normal distribution that the Moving Average assumes. As a result, the Moving Median provides a more reliable and stable estimate of the underlying trend even when the time series is affected by large deviations from the trend. Additionally, the Moving Median smoothing is identical to the Median Filter, which has various applications in image signal processing.
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2018:
1401:{\displaystyle {\begin{aligned}{\textit {SMA}}_{k,{\text{next}}}&={\frac {1}{k}}\sum _{i=n-k+2}^{n+1}p_{i}\\&={\frac {1}{k}}{\Big (}\underbrace {p_{n-k+2}+p_{n-k+3}+\dots +p_{n}+p_{n+1}} _{\sum _{i=n-k+2}^{n+1}p_{i}}+\underbrace {p_{n-k+1}-p_{n-k+1}} _{=0}{\Big )}\\&=\underbrace {{\frac {1}{k}}{\Big (}p_{n-k+1}+p_{n-k+2}+\dots +p_{n}{\Big )}} _{={\textit {SMA}}_{k,{\text{prev}}}}-{\frac {p_{n-k+1}}{k}}+{\frac {p_{n+1}}{k}}\\&={\textit {SMA}}_{k,{\text{prev}}}+{\frac {1}{k}}{\Big (}p_{n+1}-p_{n-k+1}{\Big )}\end{aligned}}}
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2065:), the data arrive in an ordered datum stream, and the user would like to get the average of all of the data up until the current datum. For example, an investor may want the average price of all of the stock transactions for a particular stock up until the current time. As each new transaction occurs, the average price at the time of the transaction can be calculated for all of the transactions up to that point using the cumulative average, typically an equally weighted
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3038:{\displaystyle {\begin{aligned}{\textit {CA}}_{n+1}&={x_{n+1}+n\cdot {\textit {CA}}_{n} \over {n+1}}\\&={x_{n+1}+(n+1-1)\cdot {\textit {CA}}_{n} \over {n+1}}\\&={(n+1)\cdot {\textit {CA}}_{n}+x_{n+1}-{\textit {CA}}_{n} \over {n+1}}\\&={{\textit {CA}}_{n}}+{{x_{n+1}-{\textit {CA}}_{n}} \over {n+1}}\end{aligned}}}
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data-points. However, in science and engineering, the mean is normally taken from an equal number of data on either side of a central value. This ensures that variations in the mean are aligned with the variations in the data rather than being shifted in time. An example of a simple equally weighted
3965:
Outside the world of finance, weighted running means have many forms and applications. Each weighting function or "kernel" has its own characteristics. In engineering and science the frequency and phase response of the filter is often of primary importance in understanding the desired and undesired
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which has applications in, for example, image signal processing. The Moving Median is a more robust alternative to the Moving
Average when it comes to estimating the underlying trend in a time series. While the Moving Average is optimal for recovering the trend if the fluctuations around the trend
123:
Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next
136:
to examine gross domestic product, employment or other macroeconomic time series. When used with non-time series data, a moving average filters higher frequency components without any specific connection to time, although typically some kind of ordering is implied. Viewed simplistically it can be
3969:
A mean does not just "smooth" the data. A mean is a form of low-pass filter. The effects of the particular filter used should be understood in order to make an appropriate choice. On this point, the French version of this article discusses the spectral effects of 3 kinds of means (cumulative,
1471:
If the data used are not centered around the mean, a simple moving average lags behind the latest datum by half the sample width. An SMA can also be disproportionately influenced by old data dropping out or new data coming in. One characteristic of the SMA is that if the data has a periodic
1687:
4116:. However, the normal distribution does not place high probability on very large deviations from the trend which explains why such deviations will have a disproportionately large effect on the trend estimate. It can be shown that if the fluctuations are instead assumed to be
3882:{\displaystyle {\begin{aligned}{\text{Total}}_{M+1}&={\text{Total}}_{M}+p_{M+1}-p_{M-n+1}\\{\text{Numerator}}_{M+1}&={\text{Numerator}}_{M}+np_{M+1}-{\text{Total}}_{M}\\{\text{WMA}}_{M+1}&={{\text{Numerator}}_{M+1} \over n+(n-1)+\cdots +2+1}\end{aligned}}}
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1868:{\displaystyle {\begin{array}{rrcl}MA_{f}:&\mathbb {R} &\rightarrow &\mathbb {R} \\&x&\mapsto &\displaystyle {\frac {1}{2\cdot \varepsilon }}\cdot \int _{x_{o}-\varepsilon }^{x_{o}+\varepsilon }f\left(t\right)\,dt\end{array}}}
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This can lead to unexpected artifacts, such as peaks in the smoothed result appearing where there were troughs in the data. It also leads to the result being less smooth than expected since some of the higher frequencies are not properly removed.
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data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. It is also used in
4120:, then the moving median is statistically optimal. For a given variance, the Laplace distribution places higher probability on rare events than does the normal, which explains why the moving median tolerates shocks better than the moving mean.
2350:), then the cumulative average will equal the final average. It is also possible to store a running total of the data as well as the number of points and dividing the total by the number of points to get the CA each time a new datum arrives.
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The brute-force method to calculate this would be to store all of the data and calculate the sum and divide by the number of points every time a new datum arrived. However, it is possible to simply update cumulative average as a new value,
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From a statistical point of view, the moving average, when used to estimate the underlying trend in a time series, is susceptible to rare events such as rapid shocks or other anomalies. A more robust estimate of the trend is the
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The graph at the right shows how the weights decrease, from highest weight for the most recent data, down to zero. It can be compared to the weights in the exponential moving average which follows.
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A weighted average is an average that has multiplying factors to give different weights to data at different positions in the sample window. Mathematically, the weighted moving average is the
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fluctuation, then applying an SMA of that period will eliminate that variation (the average always containing one complete cycle). But a perfectly regular cycle is rarely encountered.
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can be computed, using data equally spaced on either side of the point in the series where the mean is calculated. This requires using an odd number of points in the sample window.
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Those two concepts are often confused due to their name, but while they share many similarities, they represent distinct methods and are used in very different contexts.
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512:{\displaystyle {\begin{aligned}{\textit {SMA}}_{k}&={\frac {p_{n-k+1}+p_{n-k+2}+\cdots +p_{n}}{k}}\\&={\frac {1}{k}}\sum _{i=n-k+1}^{n}p_{i}\end{aligned}}}
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A further weighting, used by actuaries, is
Spencer's 15-Point Moving Average (a central moving average). Its symmetric weight coefficients are , which factors as
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Statistically, the moving average is optimal for recovering the underlying trend of the time series when the fluctuations about the trend are
1682:{\displaystyle {\begin{array}{rrcl}f:&\mathbb {R} &\rightarrow &\mathbb {R} \\&x&\mapsto &f\left(x\right)\end{array}}}
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A major drawback of the SMA is that it lets through a significant amount of the signal shorter than the window length. Worse, it
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is found by, for example, sorting the values inside the brackets and finding the value in the middle. For larger values of
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3305:{\displaystyle {\text{WMA}}_{M}={np_{M}+(n-1)p_{M-1}+\cdots +2p_{((M-n)+2)}+p_{((M-n)+1)} \over n+(n-1)+\cdots +2+1}}
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Continuous moving average sine and polynom - visualization of the smoothing with a larger interval for integration
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For a number of applications, it is advantageous to avoid the shifting induced by using only "past" data. Hence a
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Continuous moving average sine and polynom - visualization of the smoothing with a small interval for integration
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During the initial filling of the FIFO / circular buffer the sampling window is equal to the data-set size thus
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This means that the moving average filter can be computed quite cheaply on real time data with a FIFO /
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Thus the current cumulative average for a new datum is equal to the previous cumulative average, times
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more. The animations below show the moving average as animation in dependency of different values for
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4375:"Efficient Running Median using an Indexable Skiplist « Python recipes « ActiveState Code"
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decreases exponentially, never reaching zero. This formulation is according to Hunter (1986).
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G.R. Arce, "Nonlinear Signal
Processing: A Statistical Approach", Wiley:New Jersey, US, 2005.
4090:{\displaystyle {\widetilde {p}}_{\text{SM}}={\text{Median}}(p_{M},p_{M-1},\ldots ,p_{M-n+1})}
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When calculating the WMA across successive values, the difference between the numerators of
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In the more general case the denominator will always be the sum of the individual weights.
3067:(WMA) has the specific meaning of weights that decrease in arithmetical progression. In an
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1468:) depends on the type of movement of interest, such as short, intermediate, or long-term.
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The derivation and properties of the simple central moving average are given in full at
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2323:{\displaystyle {\textit {CA}}_{n+1}={{x_{n+1}+n\cdot {\textit {CA}}_{n}} \over {n+1}}.}
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4240:(Booth et al., San Francisco Estuary and Watershed Science, Volume 4, Issue 2, 2006)
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In the financial field, and more specifically in the analyses of financial data, a
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When the simple moving median above is central, the smoothing is identical to the
2633:{\displaystyle x_{n+1}=(n+1)\cdot {\textit {CA}}_{n+1}-n\cdot {\textit {CA}}_{n}}
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Other weighting systems are used occasionally â for example, in share trading a
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Animation showing the impact of interval width and smoothing by moving average.
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NIST/SEMATECH e-Handbook of
Statistical Methods: Single Exponential Smoothing
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The derivation of the cumulative average formula is straightforward. Using
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The continuous moving average is defined with the following integral. The
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of the data with a fixed weighting function. One application is removing
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drops out. This simplifies the calculations by reusing the previous mean
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Smoothing of a noisy sine (blue curve) with a moving average (red curve).
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2537:{\displaystyle x_{n+1}=(x_{1}+\cdots +x_{n+1})-(x_{1}+\cdots +x_{n})}
2188:{\displaystyle {\textit {CA}}_{n}={{x_{1}+\cdots +x_{n}} \over n}\,.}
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and leaves samples of any quadratic or cubic polynomial unchanged.
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will weight each time period in proportion to its trading volume.
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284:. This could be closing prices of a stock. The mean over the last
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of different selections of the full data set. Variations include:
74:) is a calculation to analyze data points by creating a series of
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defines the intensity of smoothing of the graph of the function.
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distortions that a particular filter will apply to the data.
2415:{\displaystyle x_{1}+\cdots +x_{n}=n\cdot {\textit {CA}}_{n}}
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Its frequency response is a type of low-pass filter called
4337:
Spencer's 15-Point Moving
Average â from Wolfram MathWorld
4105:, the median can be efficiently computed by updating an
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Hydrologic
Variability of the Cosumnes River Floodplain
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filter that applies weighting factors which decrease
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4298:"DEALING WITH MEASUREMENT NOISE - Averaging Filter"
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4362:Statistics 153 (Time Series) : Lecture Three
1904:smoothes the source graph of the function (blue)
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742:{\displaystyle {\textit {SMA}}_{k,{\text{prev}}}}
553:{\displaystyle {\textit {SMA}}_{k,{\text{next}}}}
304:data-points (days in this example) is denoted as
112:outlines its filter coefficients, it is called a
1978:{\displaystyle {\frac {1}{2\cdot \varepsilon }}}
5853:Multivariate adaptive regression splines (MARS)
4175:Moving average convergence/divergence indicator
3517:{\displaystyle np_{M+1}-p_{M}-\dots -p_{M-n+1}}
4326:National Institute of Standards and Technology
1692:The continuous moving average of the function
1441:and the average calculation is performed as a
93:Mathematically, a moving average is a type of
83:
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8:
3917:exponentially weighted moving average (EWMA)
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4252:, Ya-lun Chou, Holt International, 1975,
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2747:
2746:
2724:
2717:
2698:
2692:
2691:
2686:
2684:
2657:
2651:
2650:
2647:
2624:
2618:
2617:
2595:
2589:
2588:
2554:
2548:
2525:
2506:
2481:
2462:
2440:
2434:
2406:
2400:
2399:
2383:
2364:
2358:
2304:
2297:
2291:
2290:
2268:
2263:
2261:
2246:
2240:
2239:
2236:
2209:
2203:
2181:
2169:
2150:
2145:
2143:
2134:
2128:
2127:
2124:
2103:
2084:
2078:
1990:
1957:
1955:
1929:
1909:
1883:
1853:
1825:
1820:
1807:
1802:
1777:
1757:
1756:
1745:
1744:
1733:
1722:
1720:
1697:
1642:
1641:
1630:
1629:
1617:
1615:
1591:
1585:
1555:
1536:
1527:
1507:
1453:
1420:
1388:
1387:
1369:
1350:
1340:
1339:
1329:
1319:
1312:
1306:
1305:
1277:
1271:
1245:
1239:
1227:
1220:
1214:
1213:
1208:
1196:
1195:
1189:
1158:
1133:
1123:
1122:
1112:
1109:
1092:
1091:
1082:
1058:
1033:
1026:
1014:
998:
975:
970:
952:
939:
908:
883:
876:
869:
868:
858:
842:
826:
803:
789:
775:
768:
762:
761:
756:
754:
732:
725:
719:
718:
715:
682:
676:
649:
643:
617:
585:
565:
543:
536:
530:
529:
526:
499:
489:
466:
452:
430:
399:
374:
367:
354:
348:
347:
342:
340:
319:
313:
312:
309:
289:
268:
249:
236:
230:
210:
190:
169:
2011:is the interval width for the integral.
671:comes into the sum and the oldest value
277:{\displaystyle p_{1},p_{2},\dots ,p_{n}}
4230:
2013:
1442:
185:running mean is the mean over the last
127:A moving average is commonly used with
6379:KaplanâMeier estimator (product limit)
4284:"Weighted Moving Averages: The Basics"
3575:{\displaystyle p_{M}+\dots +p_{M-n+1}}
7078:Moving average convergence/divergence
2231:becomes available, using the formula
7:
6689:
6389:Accelerated failure time (AFT) model
2672:{\displaystyle {\textit {CA}}_{n+1}}
6701:
5984:Analysis of variance (ANOVA, anova)
4364:". 2012-01-24. Accessed 2024-01-07.
4351:". 2009-11-08. Accessed 2020-08-20.
4220:Zero lag exponential moving average
3071:-day WMA the latest day has weight
2112:{\displaystyle x_{1}.\ldots ,x_{n}}
2004:{\displaystyle 2\cdot \varepsilon }
328:{\displaystyle {\textit {SMA}}_{k}}
6079:CochranâMantelâHaenszel statistics
4705:Pearson product-moment correlation
3604:{\displaystyle {\text{Total}}_{M}}
3408:{\displaystyle {\text{WMA}}_{M+1}}
225:entries. Let those data-points be
25:
3370:{\textstyle {\frac {n(n+1)}{2}}.}
2340:+1. When all of the data arrive (
1943:{\displaystyle \varepsilon >0}
1897:{\displaystyle \varepsilon >0}
205:entries of a data-set containing
6700:
6688:
6676:
6663:
6662:
3913:exponential moving average (EMA)
3437:{\displaystyle {\text{WMA}}_{M}}
3060:from a digital graphical image.
2040:
2028:
2016:
137:regarded as smoothing the data.
7486:Statistical charts and diagrams
7448:Associative (causal) forecasts
6338:Least-squares spectral analysis
4180:Martingale (probability theory)
4144:moving average regression model
4132:Moving average regression model
3927:. The weighting for each older
521:When calculating the next mean
36:Moving average (disambiguation)
7183:Accumulation/distribution line
5319:Mean-unbiased minimum-variance
4084:
4021:
3851:
3839:
3355:
3343:
3278:
3266:
3253:
3244:
3232:
3229:
3216:
3207:
3195:
3192:
3156:
3144:
2882:
2870:
2823:
2805:
2581:
2569:
2531:
2499:
2493:
2455:
1771:
1751:
1656:
1636:
1567:
1529:
116:. It is sometimes followed by
1:
6632:Geographic information system
5848:Simultaneous equations models
1412:and only 3 arithmetic steps.
560:with the same sampling width
7433:Decomposition of time series
7259:CBOE Market Volatility Index
6900:Triple top and triple bottom
6865:Double top and double bottom
5815:Coefficient of determination
5426:Uniformly most powerful test
1515:{\displaystyle \varepsilon }
152:In financial applications a
6384:Proportional hazards models
6328:Spectral density estimation
6310:Vector autoregression (VAR)
5744:Maximum posterior estimator
4976:Randomized controlled trial
638:is considered. A new value
7517:
7414:Historical data forecasts
7067:Know sure thing oscillator
7061:Detrended price oscillator
6144:Multivariate distributions
4564:Average absolute deviation
4135:
3904:
3898:
3895:Exponential moving average
2642:Solving this equation for
29:
18:Exponential moving average
7446:
7412:
7049:Average directional index
6658:
6461:
6448:
6132:Structural equation model
6040:
6015:
5786:
5762:
5494:
5468:Score/Lagrange multiplier
5074:
5061:
4883:Sample size determination
4844:
4831:
4461:
4448:
4430:
3921:infinite impulse response
1498:Continuous Moving Average
1443:cumulative moving average
703:{\displaystyle p_{n-k+1}}
7456:Simple linear regression
6627:Environmental statistics
6149:Elliptical distributions
5942:Generalized linear model
5871:Simple linear regression
5641:HodgesâLehmann estimator
5098:Probability distribution
5007:Stochastic approximation
4569:Coefficient of variation
4185:Moving average crossover
3970:exponential, Gaussian).
2119:up to the current time:
7142:Relative strength index
7055:Commodity channel index
6287:Cross-correlation (XCF)
5895:Non-standard predictors
5329:LehmannâScheffĂ© theorem
5002:Adaptive clinical trial
3524:. If we denote the sum
3065:weighted moving average
3048:Weighted moving average
2224:{\displaystyle x_{n+1}}
664:{\displaystyle p_{n+1}}
106:finite impulse response
6779:Elliott wave principle
6683:Mathematics portal
6504:Engineering statistics
6412:NelsonâAalen estimator
5989:Analysis of covariance
5876:Ordinary least squares
5800:Pearson product-moment
5204:Statistical functional
5115:Empirical distribution
4948:Controlled experiments
4677:Frequency distribution
4455:Descriptive statistics
4091:
3883:
3605:
3576:
3518:
3438:
3409:
3371:
3323:
3306:
3095:
3039:
2673:
2634:
2538:
2416:
2324:
2225:
2189:
2113:
2005:
1979:
1944:
1918:
1898:
1869:
1706:
1683:
1601:
1574:
1516:
1477:central moving average
1462:
1435:
1402:
1009:
837:
743:
704:
665:
632:
606:
574:
554:
513:
494:
329:
298:
278:
219:
199:
178:
149:
46:
7423:Exponential smoothing
7200:Negative volume index
7148:Stochastic oscillator
7025:Fibonacci retracement
6599:Population statistics
6541:System identification
6275:Autocorrelation (ACF)
6203:Exponential smoothing
6117:Discriminant analysis
6112:Canonical correlation
5976:Partition of variance
5838:Regression validation
5682:(JonckheereâTerpstra)
5581:Likelihood-ratio test
5270:Frequentist inference
5182:Locationâscale family
5103:Sampling distribution
5068:Statistical inference
5035:Cross-sectional study
5022:Observational studies
4981:Randomized experiment
4810:Stem-and-leaf display
4612:Central limit theorem
4360:Aditya Guntuboyina. "
4271:SavitzkyâGolay filter
4210:SavitzkyâGolay filter
4195:Rising moving average
4159:Exponential smoothing
4092:
3905:Further information:
3901:Exponential smoothing
3884:
3606:
3577:
3519:
3439:
3410:
3372:
3326:The denominator is a
3317:
3307:
3101:, etc., down to one.
3096:
3040:
2674:
2635:
2539:
2417:
2325:
2226:
2190:
2114:
2006:
1980:
1945:
1919:
1899:
1870:
1707:
1684:
1602:
1600:{\displaystyle x_{o}}
1575:
1517:
1463:
1448:The period selected (
1436:
1403:
971:
799:
744:
705:
666:
633:
607:
605:{\displaystyle n-k+2}
575:
555:
514:
462:
330:
299:
279:
220:
200:
179:
154:simple moving average
148:
141:Simple moving average
124:value in the subset.
44:
7352:Ralph Nelson Elliott
7296:McClellan oscillator
7284:Advanceâdecline line
6965:Three white soldiers
6522:Probabilistic design
6107:Principal components
5950:Exponential families
5902:Nonlinear regression
5881:General linear model
5843:Mixed effects models
5833:Errors and residuals
5810:Confounding variable
5712:Bayesian probability
5690:Van der Waerden test
5680:Ordered alternative
5445:Multiple comparisons
5324:RaoâBlackwellization
5287:Estimating equations
5243:Statistical distance
4961:Factorial experiment
4494:Arithmetic-Geometric
4250:Statistical Analysis
4190:Moving least squares
4138:Moving-average model
4114:normally distributed
3991:
3981:simple moving median
3617:
3586:
3528:
3448:
3419:
3384:
3334:
3107:
3079:
3075:, the second latest
2683:
2646:
2547:
2433:
2357:
2235:
2202:
2123:
2077:
1989:
1954:
1928:
1908:
1882:
1719:
1696:
1614:
1584:
1526:
1506:
1484:actually inverts it.
1452:
1419:
753:
714:
675:
642:
616:
584:
564:
525:
339:
308:
288:
229:
209:
189:
168:
160:) is the unweighted
108:filter. Because the
32:Moving-average model
30:For other uses, see
7461:Regression analysis
7159:Ultimate oscillator
7153:True strength index
6820:Open-high-low-close
6594:Official statistics
6517:Methods engineering
6198:Seasonal adjustment
5966:Poisson regressions
5886:Bayesian regression
5825:Regression analysis
5805:Partial correlation
5777:Regression analysis
5376:Prediction interval
5371:Likelihood interval
5361:Confidence interval
5353:Interval estimation
5314:Unbiased estimators
5132:Model specification
5012:Up-and-down designs
4700:Partial correlation
4656:Index of dispersion
4574:Interquartile range
4118:Laplace distributed
3919:, is a first-order
3915:, also known as an
3094:{\displaystyle n-1}
2069:of the sequence of
1838:
1434:{\displaystyle k=n}
631:{\displaystyle n+1}
335:and calculated as:
7501:Technical analysis
7265:Standard deviation
7237:Average true range
7218:Volumeâprice trend
7073:Ichimoku KinkĆ HyĆ
6880:Head and shoulders
6750:Technical analysis
6614:Spatial statistics
6494:Medical statistics
6394:First hitting time
6348:Whittle likelihood
5999:Degrees of freedom
5994:Multivariate ANOVA
5927:Heteroscedasticity
5739:Bayesian estimator
5704:Bayesian inference
5553:KolmogorovâSmirnov
5438:Randomization test
5408:Testing hypotheses
5381:Tolerance interval
5292:Maximum likelihood
5187:Exponential family
5120:Density estimation
5080:Statistical theory
5040:Natural experiment
4986:Scientific control
4903:Survey methodology
4589:Standard deviation
4166:(LOESS and LOWESS)
4107:indexable skiplist
4087:
3879:
3877:
3601:
3572:
3514:
3434:
3405:
3367:
3324:
3302:
3091:
3035:
3033:
2669:
2630:
2534:
2429:, it is seen that
2422:and similarly for
2412:
2320:
2221:
2185:
2109:
2059:cumulative average
2053:Cumulative average
2001:
1975:
1940:
1914:
1894:
1865:
1863:
1860:
1798:
1702:
1679:
1677:
1597:
1570:
1512:
1458:
1431:
1398:
1396:
1235:
1206:
1090:
1080:
1021:
968:
739:
700:
661:
628:
602:
570:
550:
509:
507:
325:
294:
274:
215:
195:
174:
150:
101:it is viewed as a
47:
7473:
7472:
7466:Econometric model
7370:
7369:
7325:
7324:
7206:On-balance volume
7101:Smart money index
7000:
6999:
6973:
6972:
6960:Three black crows
6716:
6715:
6654:
6653:
6650:
6649:
6589:National accounts
6559:Actuarial science
6551:Social statistics
6444:
6443:
6440:
6439:
6436:
6435:
6371:Survival function
6356:
6355:
6218:Granger causality
6059:Contingency table
6034:Survival analysis
6011:
6010:
6007:
6006:
5863:Linear regression
5758:
5757:
5754:
5753:
5729:Credible interval
5698:
5697:
5481:
5480:
5297:Method of moments
5166:Parametric family
5127:Statistical model
5057:
5056:
5053:
5052:
4971:Random assignment
4893:Statistical power
4827:
4826:
4823:
4822:
4672:Contingency table
4642:
4641:
4509:Generalized/power
4019:
4010:
4004:
3873:
3818:
3791:
3775:
3738:
3713:
3653:
3628:
3593:
3426:
3391:
3362:
3300:
3114:
3029:
3008:
2968:
2950:
2929:
2893:
2855:
2834:
2771:
2750:
2695:
2654:
2621:
2592:
2403:
2315:
2294:
2243:
2179:
2131:
1985:is used, because
1973:
1917:{\displaystyle f}
1793:
1705:{\displaystyle f}
1492:sinc-in-frequency
1461:{\displaystyle k}
1337:
1322:
1309:
1292:
1266:
1230:
1217:
1120:
1110:
1108:
1027:
1025:
877:
875:
866:
797:
778:
765:
735:
722:
573:{\displaystyle k}
546:
533:
460:
440:
351:
316:
297:{\displaystyle k}
218:{\displaystyle n}
198:{\displaystyle k}
177:{\displaystyle k}
99:signal processing
16:(Redirected from
7508:
7397:
7390:
7383:
7374:
7249:Donchian channel
7188:Ease of movement
7136:Money flow index
7117:Vortex indicator
7011:
6979:Point and figure
6920:
6870:Flag and pennant
6843:
6825:Point and figure
6743:
6736:
6729:
6720:
6704:
6703:
6692:
6691:
6681:
6680:
6666:
6665:
6569:Crime statistics
6463:
6450:
6367:
6333:Fourier analysis
6320:Frequency domain
6300:
6247:
6213:Structural break
6173:
6122:Cluster analysis
6069:Log-linear model
6042:
6017:
5958:
5932:Homoscedasticity
5788:
5764:
5683:
5675:
5667:
5666:(KruskalâWallis)
5651:
5636:
5591:Cross validation
5576:
5558:AndersonâDarling
5505:
5492:
5463:Likelihood-ratio
5455:Parametric tests
5433:Permutation test
5416:1- & 2-tails
5307:Minimum distance
5279:Point estimation
5275:
5226:Optimal decision
5177:
5076:
5063:
5045:Quasi-experiment
4995:Adaptive designs
4846:
4833:
4710:Rank correlation
4472:
4463:
4450:
4417:
4410:
4403:
4394:
4388:
4385:
4379:
4378:
4371:
4365:
4358:
4352:
4347:Rob J Hyndman. "
4345:
4339:
4334:
4328:
4319:
4313:
4312:
4310:
4309:
4300:. Archived from
4294:
4288:
4287:
4280:
4274:
4267:
4261:
4247:
4241:
4235:
4170:Kernel smoothing
4164:Local regression
4096:
4094:
4093:
4088:
4083:
4082:
4052:
4051:
4033:
4032:
4020:
4017:
4012:
4011:
4008:
4006:
4005:
3997:
3961:
3959:
3958:
3955:
3952:
3941:volume weighting
3935:Other weightings
3888:
3886:
3885:
3880:
3878:
3874:
3872:
3831:
3830:
3819:
3816:
3813:
3804:
3803:
3792:
3789:
3782:
3781:
3776:
3773:
3767:
3766:
3745:
3744:
3739:
3736:
3726:
3725:
3714:
3711:
3704:
3703:
3679:
3678:
3660:
3659:
3654:
3651:
3641:
3640:
3629:
3626:
3610:
3608:
3607:
3602:
3600:
3599:
3594:
3591:
3581:
3579:
3578:
3573:
3571:
3570:
3540:
3539:
3523:
3521:
3520:
3515:
3513:
3512:
3482:
3481:
3469:
3468:
3443:
3441:
3440:
3435:
3433:
3432:
3427:
3424:
3414:
3412:
3411:
3406:
3404:
3403:
3392:
3389:
3376:
3374:
3373:
3368:
3363:
3358:
3338:
3311:
3309:
3308:
3303:
3301:
3299:
3258:
3257:
3256:
3220:
3219:
3174:
3173:
3140:
3139:
3126:
3121:
3120:
3115:
3112:
3100:
3098:
3097:
3092:
3044:
3042:
3041:
3036:
3034:
3030:
3028:
3017:
3016:
3015:
3010:
3009:
2999:
2998:
2982:
2977:
2976:
2975:
2970:
2969:
2955:
2951:
2949:
2938:
2937:
2936:
2931:
2930:
2920:
2919:
2901:
2900:
2895:
2894:
2868:
2860:
2856:
2854:
2843:
2842:
2841:
2836:
2835:
2801:
2800:
2784:
2776:
2772:
2770:
2759:
2758:
2757:
2752:
2751:
2735:
2734:
2718:
2709:
2708:
2697:
2696:
2678:
2676:
2675:
2670:
2668:
2667:
2656:
2655:
2639:
2637:
2636:
2631:
2629:
2628:
2623:
2622:
2606:
2605:
2594:
2593:
2565:
2564:
2543:
2541:
2540:
2535:
2530:
2529:
2511:
2510:
2492:
2491:
2467:
2466:
2451:
2450:
2428:
2421:
2419:
2418:
2413:
2411:
2410:
2405:
2404:
2388:
2387:
2369:
2368:
2349:
2329:
2327:
2326:
2321:
2316:
2314:
2303:
2302:
2301:
2296:
2295:
2279:
2278:
2262:
2257:
2256:
2245:
2244:
2230:
2228:
2227:
2222:
2220:
2219:
2194:
2192:
2191:
2186:
2180:
2175:
2174:
2173:
2155:
2154:
2144:
2139:
2138:
2133:
2132:
2118:
2116:
2115:
2110:
2108:
2107:
2089:
2088:
2044:
2032:
2020:
2010:
2008:
2007:
2002:
1984:
1982:
1981:
1976:
1974:
1972:
1958:
1949:
1947:
1946:
1941:
1923:
1921:
1920:
1915:
1903:
1901:
1900:
1895:
1874:
1872:
1871:
1866:
1864:
1852:
1837:
1830:
1829:
1819:
1812:
1811:
1794:
1792:
1778:
1764:
1760:
1748:
1738:
1737:
1711:
1709:
1708:
1703:
1688:
1686:
1685:
1680:
1678:
1674:
1649:
1645:
1633:
1606:
1604:
1603:
1598:
1596:
1595:
1579:
1577:
1576:
1573:{\displaystyle }
1571:
1560:
1559:
1541:
1540:
1521:
1519:
1518:
1513:
1467:
1465:
1464:
1459:
1440:
1438:
1437:
1432:
1407:
1405:
1404:
1399:
1397:
1393:
1392:
1386:
1385:
1361:
1360:
1345:
1344:
1338:
1330:
1325:
1324:
1323:
1320:
1311:
1310:
1297:
1293:
1288:
1287:
1272:
1267:
1262:
1261:
1240:
1234:
1233:
1232:
1231:
1228:
1219:
1218:
1207:
1202:
1201:
1200:
1194:
1193:
1175:
1174:
1150:
1149:
1128:
1127:
1121:
1113:
1101:
1097:
1096:
1089:
1081:
1076:
1075:
1074:
1050:
1049:
1020:
1019:
1018:
1008:
997:
969:
964:
963:
962:
944:
943:
925:
924:
900:
899:
874:
873:
867:
859:
851:
847:
846:
836:
825:
798:
790:
781:
780:
779:
776:
767:
766:
748:
746:
745:
740:
738:
737:
736:
733:
724:
723:
709:
707:
706:
701:
699:
698:
670:
668:
667:
662:
660:
659:
637:
635:
634:
629:
611:
609:
608:
603:
579:
577:
576:
571:
559:
557:
556:
551:
549:
548:
547:
544:
535:
534:
518:
516:
515:
510:
508:
504:
503:
493:
488:
461:
453:
445:
441:
436:
435:
434:
416:
415:
391:
390:
368:
359:
358:
353:
352:
334:
332:
331:
326:
324:
323:
318:
317:
303:
301:
300:
295:
283:
281:
280:
275:
273:
272:
254:
253:
241:
240:
224:
222:
221:
216:
204:
202:
201:
196:
183:
181:
180:
175:
164:of the previous
21:
7516:
7515:
7511:
7510:
7509:
7507:
7506:
7505:
7476:
7475:
7474:
7469:
7442:
7408:
7401:
7371:
7366:
7321:
7300:
7270:
7254:Keltner channel
7243:Bollinger Bands
7223:
7169:
7122:
7035:
7016:
6996:
6969:
6950:Hikkake pattern
6936:
6909:
6885:Island reversal
6834:
6788:
6769:Dead cat bounce
6752:
6747:
6717:
6712:
6675:
6646:
6608:
6545:
6531:quality control
6498:
6480:Clinical trials
6457:
6432:
6416:
6404:Hazard function
6398:
6352:
6314:
6298:
6261:
6257:BreuschâGodfrey
6245:
6222:
6162:
6137:Factor analysis
6083:
6064:Graphical model
6036:
6003:
5970:
5956:
5936:
5890:
5857:
5819:
5782:
5781:
5750:
5694:
5681:
5673:
5665:
5649:
5634:
5613:Rank statistics
5607:
5586:Model selection
5574:
5532:Goodness of fit
5526:
5503:
5477:
5449:
5402:
5347:
5336:Median unbiased
5264:
5175:
5108:Order statistic
5070:
5049:
5016:
4990:
4942:
4897:
4840:
4838:Data collection
4819:
4731:
4686:
4660:
4638:
4598:
4550:
4467:Continuous data
4457:
4444:
4426:
4421:
4391:
4386:
4382:
4373:
4372:
4368:
4359:
4355:
4349:Moving averages
4346:
4342:
4335:
4331:
4320:
4316:
4307:
4305:
4296:
4295:
4291:
4286:. Investopedia.
4282:
4281:
4277:
4268:
4264:
4260:, section 17.9.
4248:
4244:
4236:
4232:
4228:
4215:Window function
4155:
4140:
4134:
4062:
4037:
4024:
3994:
3989:
3988:
3976:
3956:
3953:
3950:
3949:
3947:
3937:
3909:
3903:
3897:
3876:
3875:
3832:
3814:
3805:
3787:
3784:
3783:
3771:
3752:
3734:
3727:
3709:
3706:
3705:
3683:
3664:
3649:
3642:
3624:
3615:
3614:
3589:
3584:
3583:
3550:
3531:
3526:
3525:
3492:
3473:
3454:
3446:
3445:
3422:
3417:
3416:
3387:
3382:
3381:
3339:
3332:
3331:
3328:triangle number
3259:
3224:
3187:
3159:
3131:
3127:
3110:
3105:
3104:
3077:
3076:
3050:
3032:
3031:
3003:
2984:
2963:
2953:
2952:
2924:
2905:
2888:
2869:
2858:
2857:
2829:
2786:
2785:
2774:
2773:
2745:
2720:
2719:
2710:
2690:
2681:
2680:
2649:
2644:
2643:
2616:
2587:
2550:
2545:
2544:
2521:
2502:
2477:
2458:
2436:
2431:
2430:
2423:
2398:
2379:
2360:
2355:
2354:
2341:
2289:
2264:
2238:
2233:
2232:
2205:
2200:
2199:
2165:
2146:
2126:
2121:
2120:
2099:
2080:
2075:
2074:
2055:
2048:
2045:
2036:
2033:
2024:
2021:
1987:
1986:
1962:
1952:
1951:
1950:. The fraction
1926:
1925:
1906:
1905:
1880:
1879:
1862:
1861:
1842:
1821:
1803:
1782:
1774:
1769:
1762:
1761:
1754:
1749:
1742:
1729:
1717:
1716:
1712:is defined as:
1694:
1693:
1676:
1675:
1664:
1659:
1654:
1647:
1646:
1639:
1634:
1627:
1612:
1611:
1587:
1582:
1581:
1551:
1532:
1524:
1523:
1504:
1503:
1500:
1450:
1449:
1417:
1416:
1410:circular buffer
1395:
1394:
1365:
1346:
1304:
1295:
1294:
1273:
1241:
1212:
1185:
1154:
1129:
1111:
1099:
1098:
1054:
1029:
1028:
1010:
948:
935:
904:
879:
878:
849:
848:
838:
782:
760:
751:
750:
717:
712:
711:
678:
673:
672:
645:
640:
639:
614:
613:
582:
581:
580:the range from
562:
561:
528:
523:
522:
506:
505:
495:
443:
442:
426:
395:
370:
369:
360:
346:
337:
336:
311:
306:
305:
286:
285:
264:
245:
232:
227:
226:
207:
206:
187:
186:
166:
165:
143:
110:boxcar function
64:running average
60:rolling average
49:
39:
28:
23:
22:
15:
12:
11:
5:
7514:
7512:
7504:
7503:
7498:
7496:Chart overlays
7493:
7488:
7478:
7477:
7471:
7470:
7468:
7463:
7458:
7453:
7451:Moving average
7447:
7444:
7443:
7441:
7440:
7438:NaĂŻve approach
7435:
7430:
7428:Trend analysis
7425:
7420:
7418:Moving average
7413:
7410:
7409:
7402:
7400:
7399:
7392:
7385:
7377:
7368:
7367:
7365:
7364:
7359:
7354:
7349:
7344:
7339:
7337:John Bollinger
7333:
7331:
7327:
7326:
7323:
7322:
7320:
7319:
7314:
7308:
7306:
7302:
7301:
7299:
7298:
7293:
7287:
7280:
7278:
7272:
7271:
7269:
7268:
7262:
7256:
7251:
7246:
7240:
7233:
7231:
7225:
7224:
7222:
7221:
7215:
7212:Put/call ratio
7209:
7203:
7197:
7191:
7185:
7179:
7177:
7171:
7170:
7168:
7167:
7161:
7156:
7150:
7145:
7139:
7132:
7130:
7124:
7123:
7121:
7120:
7114:
7109:
7104:
7098:
7092:
7089:Moving average
7086:
7081:
7075:
7070:
7064:
7058:
7052:
7045:
7043:
7037:
7036:
7034:
7033:
7027:
7021:
7019:
7008:
7002:
7001:
6998:
6997:
6995:
6994:
6989:
6983:
6981:
6975:
6974:
6971:
6970:
6968:
6967:
6962:
6957:
6952:
6946:
6944:
6938:
6937:
6935:
6934:
6928:
6926:
6917:
6911:
6910:
6908:
6907:
6902:
6897:
6892:
6890:Price channels
6887:
6882:
6877:
6872:
6867:
6862:
6860:Cup and handle
6857:
6855:Broadening top
6851:
6849:
6840:
6836:
6835:
6833:
6832:
6827:
6822:
6817:
6812:
6807:
6802:
6796:
6794:
6790:
6789:
6787:
6786:
6781:
6776:
6771:
6766:
6760:
6758:
6754:
6753:
6748:
6746:
6745:
6738:
6731:
6723:
6714:
6713:
6711:
6710:
6698:
6686:
6672:
6659:
6656:
6655:
6652:
6651:
6648:
6647:
6645:
6644:
6639:
6634:
6629:
6624:
6618:
6616:
6610:
6609:
6607:
6606:
6601:
6596:
6591:
6586:
6581:
6576:
6571:
6566:
6561:
6555:
6553:
6547:
6546:
6544:
6543:
6538:
6533:
6524:
6519:
6514:
6508:
6506:
6500:
6499:
6497:
6496:
6491:
6486:
6477:
6475:Bioinformatics
6471:
6469:
6459:
6458:
6453:
6446:
6445:
6442:
6441:
6438:
6437:
6434:
6433:
6431:
6430:
6424:
6422:
6418:
6417:
6415:
6414:
6408:
6406:
6400:
6399:
6397:
6396:
6391:
6386:
6381:
6375:
6373:
6364:
6358:
6357:
6354:
6353:
6351:
6350:
6345:
6340:
6335:
6330:
6324:
6322:
6316:
6315:
6313:
6312:
6307:
6302:
6294:
6289:
6284:
6283:
6282:
6280:partial (PACF)
6271:
6269:
6263:
6262:
6260:
6259:
6254:
6249:
6241:
6236:
6230:
6228:
6227:Specific tests
6224:
6223:
6221:
6220:
6215:
6210:
6205:
6200:
6195:
6190:
6185:
6179:
6177:
6170:
6164:
6163:
6161:
6160:
6159:
6158:
6157:
6156:
6141:
6140:
6139:
6129:
6127:Classification
6124:
6119:
6114:
6109:
6104:
6099:
6093:
6091:
6085:
6084:
6082:
6081:
6076:
6074:McNemar's test
6071:
6066:
6061:
6056:
6050:
6048:
6038:
6037:
6020:
6013:
6012:
6009:
6008:
6005:
6004:
6002:
6001:
5996:
5991:
5986:
5980:
5978:
5972:
5971:
5969:
5968:
5952:
5946:
5944:
5938:
5937:
5935:
5934:
5929:
5924:
5919:
5914:
5912:Semiparametric
5909:
5904:
5898:
5896:
5892:
5891:
5889:
5888:
5883:
5878:
5873:
5867:
5865:
5859:
5858:
5856:
5855:
5850:
5845:
5840:
5835:
5829:
5827:
5821:
5820:
5818:
5817:
5812:
5807:
5802:
5796:
5794:
5784:
5783:
5780:
5779:
5774:
5768:
5767:
5760:
5759:
5756:
5755:
5752:
5751:
5749:
5748:
5747:
5746:
5736:
5731:
5726:
5725:
5724:
5719:
5708:
5706:
5700:
5699:
5696:
5695:
5693:
5692:
5687:
5686:
5685:
5677:
5669:
5653:
5650:(MannâWhitney)
5645:
5644:
5643:
5630:
5629:
5628:
5617:
5615:
5609:
5608:
5606:
5605:
5604:
5603:
5598:
5593:
5583:
5578:
5575:(ShapiroâWilk)
5570:
5565:
5560:
5555:
5550:
5542:
5536:
5534:
5528:
5527:
5525:
5524:
5516:
5507:
5495:
5489:
5487:Specific tests
5483:
5482:
5479:
5478:
5476:
5475:
5470:
5465:
5459:
5457:
5451:
5450:
5448:
5447:
5442:
5441:
5440:
5430:
5429:
5428:
5418:
5412:
5410:
5404:
5403:
5401:
5400:
5399:
5398:
5393:
5383:
5378:
5373:
5368:
5363:
5357:
5355:
5349:
5348:
5346:
5345:
5340:
5339:
5338:
5333:
5332:
5331:
5326:
5311:
5310:
5309:
5304:
5299:
5294:
5283:
5281:
5272:
5266:
5265:
5263:
5262:
5257:
5252:
5251:
5250:
5240:
5235:
5234:
5233:
5223:
5222:
5221:
5216:
5211:
5201:
5196:
5191:
5190:
5189:
5184:
5179:
5163:
5162:
5161:
5156:
5151:
5141:
5140:
5139:
5134:
5124:
5123:
5122:
5112:
5111:
5110:
5100:
5095:
5090:
5084:
5082:
5072:
5071:
5066:
5059:
5058:
5055:
5054:
5051:
5050:
5048:
5047:
5042:
5037:
5032:
5026:
5024:
5018:
5017:
5015:
5014:
5009:
5004:
4998:
4996:
4992:
4991:
4989:
4988:
4983:
4978:
4973:
4968:
4963:
4958:
4952:
4950:
4944:
4943:
4941:
4940:
4938:Standard error
4935:
4930:
4925:
4924:
4923:
4918:
4907:
4905:
4899:
4898:
4896:
4895:
4890:
4885:
4880:
4875:
4870:
4868:Optimal design
4865:
4860:
4854:
4852:
4842:
4841:
4836:
4829:
4828:
4825:
4824:
4821:
4820:
4818:
4817:
4812:
4807:
4802:
4797:
4792:
4787:
4782:
4777:
4772:
4767:
4762:
4757:
4752:
4747:
4741:
4739:
4733:
4732:
4730:
4729:
4724:
4723:
4722:
4717:
4707:
4702:
4696:
4694:
4688:
4687:
4685:
4684:
4679:
4674:
4668:
4666:
4665:Summary tables
4662:
4661:
4659:
4658:
4652:
4650:
4644:
4643:
4640:
4639:
4637:
4636:
4635:
4634:
4629:
4624:
4614:
4608:
4606:
4600:
4599:
4597:
4596:
4591:
4586:
4581:
4576:
4571:
4566:
4560:
4558:
4552:
4551:
4549:
4548:
4543:
4538:
4537:
4536:
4531:
4526:
4521:
4516:
4511:
4506:
4501:
4499:Contraharmonic
4496:
4491:
4480:
4478:
4469:
4459:
4458:
4453:
4446:
4445:
4443:
4442:
4437:
4431:
4428:
4427:
4422:
4420:
4419:
4412:
4405:
4397:
4390:
4389:
4380:
4366:
4353:
4340:
4329:
4314:
4289:
4275:
4262:
4242:
4229:
4227:
4224:
4223:
4222:
4217:
4212:
4207:
4202:
4197:
4192:
4187:
4182:
4177:
4172:
4167:
4161:
4154:
4151:
4136:Main article:
4133:
4130:
4086:
4081:
4078:
4075:
4072:
4069:
4065:
4061:
4058:
4055:
4050:
4047:
4044:
4040:
4036:
4031:
4027:
4023:
4015:
4003:
4000:
3975:
3972:
3936:
3933:
3899:Main article:
3896:
3893:
3871:
3868:
3865:
3862:
3859:
3856:
3853:
3850:
3847:
3844:
3841:
3838:
3835:
3829:
3826:
3823:
3811:
3808:
3806:
3802:
3799:
3796:
3786:
3785:
3780:
3770:
3765:
3762:
3759:
3755:
3751:
3748:
3743:
3733:
3730:
3728:
3724:
3721:
3718:
3708:
3707:
3702:
3699:
3696:
3693:
3690:
3686:
3682:
3677:
3674:
3671:
3667:
3663:
3658:
3648:
3645:
3643:
3639:
3636:
3633:
3623:
3622:
3598:
3569:
3566:
3563:
3560:
3557:
3553:
3549:
3546:
3543:
3538:
3534:
3511:
3508:
3505:
3502:
3499:
3495:
3491:
3488:
3485:
3480:
3476:
3472:
3467:
3464:
3461:
3457:
3453:
3431:
3402:
3399:
3396:
3366:
3361:
3357:
3354:
3351:
3348:
3345:
3342:
3298:
3295:
3292:
3289:
3286:
3283:
3280:
3277:
3274:
3271:
3268:
3265:
3262:
3255:
3252:
3249:
3246:
3243:
3240:
3237:
3234:
3231:
3227:
3223:
3218:
3215:
3212:
3209:
3206:
3203:
3200:
3197:
3194:
3190:
3186:
3183:
3180:
3177:
3172:
3169:
3166:
3162:
3158:
3155:
3152:
3149:
3146:
3143:
3138:
3134:
3130:
3124:
3119:
3090:
3087:
3084:
3049:
3046:
3027:
3024:
3021:
3014:
3002:
2997:
2994:
2991:
2987:
2980:
2974:
2961:
2958:
2956:
2954:
2948:
2945:
2942:
2935:
2923:
2918:
2915:
2912:
2908:
2904:
2899:
2887:
2884:
2881:
2878:
2875:
2872:
2866:
2863:
2861:
2859:
2853:
2850:
2847:
2840:
2828:
2825:
2822:
2819:
2816:
2813:
2810:
2807:
2804:
2799:
2796:
2793:
2789:
2782:
2779:
2777:
2775:
2769:
2766:
2763:
2756:
2744:
2741:
2738:
2733:
2730:
2727:
2723:
2716:
2713:
2711:
2707:
2704:
2701:
2689:
2688:
2666:
2663:
2660:
2627:
2615:
2612:
2609:
2604:
2601:
2598:
2586:
2583:
2580:
2577:
2574:
2571:
2568:
2563:
2560:
2557:
2553:
2533:
2528:
2524:
2520:
2517:
2514:
2509:
2505:
2501:
2498:
2495:
2490:
2487:
2484:
2480:
2476:
2473:
2470:
2465:
2461:
2457:
2454:
2449:
2446:
2443:
2439:
2409:
2397:
2394:
2391:
2386:
2382:
2378:
2375:
2372:
2367:
2363:
2319:
2313:
2310:
2307:
2300:
2288:
2285:
2282:
2277:
2274:
2271:
2267:
2260:
2255:
2252:
2249:
2218:
2215:
2212:
2208:
2184:
2178:
2172:
2168:
2164:
2161:
2158:
2153:
2149:
2142:
2137:
2106:
2102:
2098:
2095:
2092:
2087:
2083:
2054:
2051:
2050:
2049:
2046:
2039:
2037:
2034:
2027:
2025:
2022:
2015:
2000:
1997:
1994:
1971:
1968:
1965:
1961:
1939:
1936:
1933:
1913:
1893:
1890:
1887:
1876:
1875:
1859:
1856:
1851:
1848:
1845:
1841:
1836:
1833:
1828:
1824:
1818:
1815:
1810:
1806:
1801:
1797:
1791:
1788:
1785:
1781:
1775:
1773:
1770:
1768:
1765:
1763:
1759:
1755:
1753:
1750:
1747:
1743:
1741:
1736:
1732:
1728:
1725:
1724:
1701:
1690:
1689:
1673:
1670:
1667:
1663:
1660:
1658:
1655:
1653:
1650:
1648:
1644:
1640:
1638:
1635:
1632:
1628:
1626:
1623:
1620:
1619:
1594:
1590:
1569:
1566:
1563:
1558:
1554:
1550:
1547:
1544:
1539:
1535:
1531:
1511:
1499:
1496:
1457:
1430:
1427:
1424:
1391:
1384:
1381:
1378:
1375:
1372:
1368:
1364:
1359:
1356:
1353:
1349:
1343:
1336:
1333:
1328:
1318:
1315:
1303:
1300:
1298:
1296:
1291:
1286:
1283:
1280:
1276:
1270:
1265:
1260:
1257:
1254:
1251:
1248:
1244:
1238:
1226:
1223:
1211:
1205:
1199:
1192:
1188:
1184:
1181:
1178:
1173:
1170:
1167:
1164:
1161:
1157:
1153:
1148:
1145:
1142:
1139:
1136:
1132:
1126:
1119:
1116:
1107:
1104:
1102:
1100:
1095:
1088:
1085:
1079:
1073:
1070:
1067:
1064:
1061:
1057:
1053:
1048:
1045:
1042:
1039:
1036:
1032:
1024:
1017:
1013:
1007:
1004:
1001:
996:
993:
990:
987:
984:
981:
978:
974:
967:
961:
958:
955:
951:
947:
942:
938:
934:
931:
928:
923:
920:
917:
914:
911:
907:
903:
898:
895:
892:
889:
886:
882:
872:
865:
862:
857:
854:
852:
850:
845:
841:
835:
832:
829:
824:
821:
818:
815:
812:
809:
806:
802:
796:
793:
788:
785:
783:
774:
771:
759:
758:
731:
728:
697:
694:
691:
688:
685:
681:
658:
655:
652:
648:
627:
624:
621:
601:
598:
595:
592:
589:
569:
542:
539:
502:
498:
492:
487:
484:
481:
478:
475:
472:
469:
465:
459:
456:
451:
448:
446:
444:
439:
433:
429:
425:
422:
419:
414:
411:
408:
405:
402:
398:
394:
389:
386:
383:
380:
377:
373:
366:
363:
361:
357:
345:
344:
322:
293:
271:
267:
263:
260:
257:
252:
248:
244:
239:
235:
214:
194:
173:
142:
139:
56:moving average
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
7513:
7502:
7499:
7497:
7494:
7492:
7489:
7487:
7484:
7483:
7481:
7467:
7464:
7462:
7459:
7457:
7454:
7452:
7449:
7445:
7439:
7436:
7434:
7431:
7429:
7426:
7424:
7421:
7419:
7416:
7415:
7411:
7406:
7403:Quantitative
7398:
7393:
7391:
7386:
7384:
7379:
7378:
7375:
7363:
7360:
7358:
7355:
7353:
7350:
7348:
7345:
7343:
7340:
7338:
7335:
7334:
7332:
7328:
7318:
7315:
7313:
7312:Coppock curve
7310:
7309:
7307:
7303:
7297:
7294:
7291:
7288:
7285:
7282:
7281:
7279:
7277:
7273:
7266:
7263:
7260:
7257:
7255:
7252:
7250:
7247:
7244:
7241:
7238:
7235:
7234:
7232:
7230:
7226:
7219:
7216:
7213:
7210:
7207:
7204:
7201:
7198:
7195:
7192:
7189:
7186:
7184:
7181:
7180:
7178:
7176:
7172:
7165:
7162:
7160:
7157:
7154:
7151:
7149:
7146:
7143:
7140:
7137:
7134:
7133:
7131:
7129:
7125:
7118:
7115:
7113:
7110:
7108:
7105:
7102:
7099:
7096:
7095:Parabolic SAR
7093:
7090:
7087:
7085:
7082:
7079:
7076:
7074:
7071:
7068:
7065:
7062:
7059:
7056:
7053:
7050:
7047:
7046:
7044:
7042:
7038:
7031:
7028:
7026:
7023:
7022:
7020:
7018:
7015:Support &
7012:
7009:
7007:
7003:
6993:
6990:
6988:
6985:
6984:
6982:
6980:
6976:
6966:
6963:
6961:
6958:
6956:
6953:
6951:
6948:
6947:
6945:
6943:
6939:
6933:
6930:
6929:
6927:
6925:
6921:
6918:
6916:
6912:
6906:
6905:Wedge pattern
6903:
6901:
6898:
6896:
6893:
6891:
6888:
6886:
6883:
6881:
6878:
6876:
6873:
6871:
6868:
6866:
6863:
6861:
6858:
6856:
6853:
6852:
6850:
6848:
6844:
6841:
6837:
6831:
6828:
6826:
6823:
6821:
6818:
6816:
6813:
6811:
6808:
6806:
6803:
6801:
6798:
6797:
6795:
6791:
6785:
6782:
6780:
6777:
6775:
6772:
6770:
6767:
6765:
6762:
6761:
6759:
6755:
6751:
6744:
6739:
6737:
6732:
6730:
6725:
6724:
6721:
6709:
6708:
6699:
6697:
6696:
6687:
6685:
6684:
6679:
6673:
6671:
6670:
6661:
6660:
6657:
6643:
6640:
6638:
6637:Geostatistics
6635:
6633:
6630:
6628:
6625:
6623:
6620:
6619:
6617:
6615:
6611:
6605:
6604:Psychometrics
6602:
6600:
6597:
6595:
6592:
6590:
6587:
6585:
6582:
6580:
6577:
6575:
6572:
6570:
6567:
6565:
6562:
6560:
6557:
6556:
6554:
6552:
6548:
6542:
6539:
6537:
6534:
6532:
6528:
6525:
6523:
6520:
6518:
6515:
6513:
6510:
6509:
6507:
6505:
6501:
6495:
6492:
6490:
6487:
6485:
6481:
6478:
6476:
6473:
6472:
6470:
6468:
6467:Biostatistics
6464:
6460:
6456:
6451:
6447:
6429:
6428:Log-rank test
6426:
6425:
6423:
6419:
6413:
6410:
6409:
6407:
6405:
6401:
6395:
6392:
6390:
6387:
6385:
6382:
6380:
6377:
6376:
6374:
6372:
6368:
6365:
6363:
6359:
6349:
6346:
6344:
6341:
6339:
6336:
6334:
6331:
6329:
6326:
6325:
6323:
6321:
6317:
6311:
6308:
6306:
6303:
6301:
6299:(BoxâJenkins)
6295:
6293:
6290:
6288:
6285:
6281:
6278:
6277:
6276:
6273:
6272:
6270:
6268:
6264:
6258:
6255:
6253:
6252:DurbinâWatson
6250:
6248:
6242:
6240:
6237:
6235:
6234:DickeyâFuller
6232:
6231:
6229:
6225:
6219:
6216:
6214:
6211:
6209:
6208:Cointegration
6206:
6204:
6201:
6199:
6196:
6194:
6191:
6189:
6186:
6184:
6183:Decomposition
6181:
6180:
6178:
6174:
6171:
6169:
6165:
6155:
6152:
6151:
6150:
6147:
6146:
6145:
6142:
6138:
6135:
6134:
6133:
6130:
6128:
6125:
6123:
6120:
6118:
6115:
6113:
6110:
6108:
6105:
6103:
6100:
6098:
6095:
6094:
6092:
6090:
6086:
6080:
6077:
6075:
6072:
6070:
6067:
6065:
6062:
6060:
6057:
6055:
6054:Cohen's kappa
6052:
6051:
6049:
6047:
6043:
6039:
6035:
6031:
6027:
6023:
6018:
6014:
6000:
5997:
5995:
5992:
5990:
5987:
5985:
5982:
5981:
5979:
5977:
5973:
5967:
5963:
5959:
5953:
5951:
5948:
5947:
5945:
5943:
5939:
5933:
5930:
5928:
5925:
5923:
5920:
5918:
5915:
5913:
5910:
5908:
5907:Nonparametric
5905:
5903:
5900:
5899:
5897:
5893:
5887:
5884:
5882:
5879:
5877:
5874:
5872:
5869:
5868:
5866:
5864:
5860:
5854:
5851:
5849:
5846:
5844:
5841:
5839:
5836:
5834:
5831:
5830:
5828:
5826:
5822:
5816:
5813:
5811:
5808:
5806:
5803:
5801:
5798:
5797:
5795:
5793:
5789:
5785:
5778:
5775:
5773:
5770:
5769:
5765:
5761:
5745:
5742:
5741:
5740:
5737:
5735:
5732:
5730:
5727:
5723:
5720:
5718:
5715:
5714:
5713:
5710:
5709:
5707:
5705:
5701:
5691:
5688:
5684:
5678:
5676:
5670:
5668:
5662:
5661:
5660:
5657:
5656:Nonparametric
5654:
5652:
5646:
5642:
5639:
5638:
5637:
5631:
5627:
5626:Sample median
5624:
5623:
5622:
5619:
5618:
5616:
5614:
5610:
5602:
5599:
5597:
5594:
5592:
5589:
5588:
5587:
5584:
5582:
5579:
5577:
5571:
5569:
5566:
5564:
5561:
5559:
5556:
5554:
5551:
5549:
5547:
5543:
5541:
5538:
5537:
5535:
5533:
5529:
5523:
5521:
5517:
5515:
5513:
5508:
5506:
5501:
5497:
5496:
5493:
5490:
5488:
5484:
5474:
5471:
5469:
5466:
5464:
5461:
5460:
5458:
5456:
5452:
5446:
5443:
5439:
5436:
5435:
5434:
5431:
5427:
5424:
5423:
5422:
5419:
5417:
5414:
5413:
5411:
5409:
5405:
5397:
5394:
5392:
5389:
5388:
5387:
5384:
5382:
5379:
5377:
5374:
5372:
5369:
5367:
5364:
5362:
5359:
5358:
5356:
5354:
5350:
5344:
5341:
5337:
5334:
5330:
5327:
5325:
5322:
5321:
5320:
5317:
5316:
5315:
5312:
5308:
5305:
5303:
5300:
5298:
5295:
5293:
5290:
5289:
5288:
5285:
5284:
5282:
5280:
5276:
5273:
5271:
5267:
5261:
5258:
5256:
5253:
5249:
5246:
5245:
5244:
5241:
5239:
5236:
5232:
5231:loss function
5229:
5228:
5227:
5224:
5220:
5217:
5215:
5212:
5210:
5207:
5206:
5205:
5202:
5200:
5197:
5195:
5192:
5188:
5185:
5183:
5180:
5178:
5172:
5169:
5168:
5167:
5164:
5160:
5157:
5155:
5152:
5150:
5147:
5146:
5145:
5142:
5138:
5135:
5133:
5130:
5129:
5128:
5125:
5121:
5118:
5117:
5116:
5113:
5109:
5106:
5105:
5104:
5101:
5099:
5096:
5094:
5091:
5089:
5086:
5085:
5083:
5081:
5077:
5073:
5069:
5064:
5060:
5046:
5043:
5041:
5038:
5036:
5033:
5031:
5028:
5027:
5025:
5023:
5019:
5013:
5010:
5008:
5005:
5003:
5000:
4999:
4997:
4993:
4987:
4984:
4982:
4979:
4977:
4974:
4972:
4969:
4967:
4964:
4962:
4959:
4957:
4954:
4953:
4951:
4949:
4945:
4939:
4936:
4934:
4933:Questionnaire
4931:
4929:
4926:
4922:
4919:
4917:
4914:
4913:
4912:
4909:
4908:
4906:
4904:
4900:
4894:
4891:
4889:
4886:
4884:
4881:
4879:
4876:
4874:
4871:
4869:
4866:
4864:
4861:
4859:
4856:
4855:
4853:
4851:
4847:
4843:
4839:
4834:
4830:
4816:
4813:
4811:
4808:
4806:
4803:
4801:
4798:
4796:
4793:
4791:
4788:
4786:
4783:
4781:
4778:
4776:
4773:
4771:
4768:
4766:
4763:
4761:
4760:Control chart
4758:
4756:
4753:
4751:
4748:
4746:
4743:
4742:
4740:
4738:
4734:
4728:
4725:
4721:
4718:
4716:
4713:
4712:
4711:
4708:
4706:
4703:
4701:
4698:
4697:
4695:
4693:
4689:
4683:
4680:
4678:
4675:
4673:
4670:
4669:
4667:
4663:
4657:
4654:
4653:
4651:
4649:
4645:
4633:
4630:
4628:
4625:
4623:
4620:
4619:
4618:
4615:
4613:
4610:
4609:
4607:
4605:
4601:
4595:
4592:
4590:
4587:
4585:
4582:
4580:
4577:
4575:
4572:
4570:
4567:
4565:
4562:
4561:
4559:
4557:
4553:
4547:
4544:
4542:
4539:
4535:
4532:
4530:
4527:
4525:
4522:
4520:
4517:
4515:
4512:
4510:
4507:
4505:
4502:
4500:
4497:
4495:
4492:
4490:
4487:
4486:
4485:
4482:
4481:
4479:
4477:
4473:
4470:
4468:
4464:
4460:
4456:
4451:
4447:
4441:
4438:
4436:
4433:
4432:
4429:
4425:
4418:
4413:
4411:
4406:
4404:
4399:
4398:
4395:
4384:
4381:
4376:
4370:
4367:
4363:
4357:
4354:
4350:
4344:
4341:
4338:
4333:
4330:
4327:
4323:
4318:
4315:
4304:on 2010-03-29
4303:
4299:
4293:
4290:
4285:
4279:
4276:
4272:
4266:
4263:
4259:
4258:0-03-089422-0
4255:
4251:
4246:
4243:
4239:
4234:
4231:
4225:
4221:
4218:
4216:
4213:
4211:
4208:
4206:
4205:Running total
4203:
4201:
4198:
4196:
4193:
4191:
4188:
4186:
4183:
4181:
4178:
4176:
4173:
4171:
4168:
4165:
4162:
4160:
4157:
4156:
4152:
4150:
4147:
4145:
4139:
4131:
4129:
4126:
4125:median filter
4121:
4119:
4115:
4110:
4108:
4104:
4100:
4079:
4076:
4073:
4070:
4067:
4063:
4059:
4056:
4053:
4048:
4045:
4042:
4038:
4034:
4029:
4025:
4013:
4001:
3998:
3987:time points:
3986:
3982:
3974:Moving median
3973:
3971:
3967:
3963:
3944:
3942:
3934:
3932:
3930:
3926:
3925:exponentially
3922:
3918:
3914:
3908:
3902:
3894:
3892:
3889:
3869:
3866:
3863:
3860:
3857:
3854:
3848:
3845:
3842:
3836:
3833:
3827:
3824:
3821:
3809:
3807:
3800:
3797:
3794:
3778:
3768:
3763:
3760:
3757:
3753:
3749:
3746:
3741:
3731:
3729:
3722:
3719:
3716:
3700:
3697:
3694:
3691:
3688:
3684:
3680:
3675:
3672:
3669:
3665:
3661:
3656:
3646:
3644:
3637:
3634:
3631:
3612:
3596:
3567:
3564:
3561:
3558:
3555:
3551:
3547:
3544:
3541:
3536:
3532:
3509:
3506:
3503:
3500:
3497:
3493:
3489:
3486:
3483:
3478:
3474:
3470:
3465:
3462:
3459:
3455:
3451:
3429:
3400:
3397:
3394:
3378:
3364:
3359:
3352:
3349:
3346:
3340:
3329:
3321:
3316:
3312:
3296:
3293:
3290:
3287:
3284:
3281:
3275:
3272:
3269:
3263:
3260:
3250:
3247:
3241:
3238:
3235:
3225:
3221:
3213:
3210:
3204:
3201:
3198:
3188:
3184:
3181:
3178:
3175:
3170:
3167:
3164:
3160:
3153:
3150:
3147:
3141:
3136:
3132:
3128:
3122:
3117:
3102:
3088:
3085:
3082:
3074:
3070:
3066:
3061:
3059:
3055:
3047:
3045:
3025:
3022:
3019:
3012:
3000:
2995:
2992:
2989:
2985:
2978:
2972:
2959:
2957:
2946:
2943:
2940:
2933:
2921:
2916:
2913:
2910:
2906:
2902:
2897:
2885:
2879:
2876:
2873:
2864:
2862:
2851:
2848:
2845:
2838:
2826:
2820:
2817:
2814:
2811:
2808:
2802:
2797:
2794:
2791:
2787:
2780:
2778:
2767:
2764:
2761:
2754:
2742:
2739:
2736:
2731:
2728:
2725:
2721:
2714:
2712:
2705:
2702:
2699:
2664:
2661:
2658:
2640:
2625:
2613:
2610:
2607:
2602:
2599:
2596:
2584:
2578:
2575:
2572:
2566:
2561:
2558:
2555:
2551:
2526:
2522:
2518:
2515:
2512:
2507:
2503:
2496:
2488:
2485:
2482:
2478:
2474:
2471:
2468:
2463:
2459:
2452:
2447:
2444:
2441:
2437:
2426:
2407:
2395:
2392:
2389:
2384:
2380:
2376:
2373:
2370:
2365:
2361:
2351:
2348:
2344:
2339:
2335:
2330:
2317:
2311:
2308:
2305:
2298:
2286:
2283:
2280:
2275:
2272:
2269:
2265:
2258:
2253:
2250:
2247:
2216:
2213:
2210:
2206:
2195:
2182:
2176:
2170:
2166:
2162:
2159:
2156:
2151:
2147:
2140:
2135:
2104:
2100:
2096:
2093:
2090:
2085:
2081:
2072:
2068:
2064:
2060:
2052:
2043:
2038:
2031:
2026:
2019:
2014:
2012:
1998:
1995:
1992:
1969:
1966:
1963:
1959:
1937:
1934:
1931:
1911:
1891:
1888:
1885:
1857:
1854:
1849:
1846:
1843:
1839:
1834:
1831:
1826:
1822:
1816:
1813:
1808:
1804:
1799:
1795:
1789:
1786:
1783:
1779:
1766:
1739:
1734:
1730:
1726:
1715:
1714:
1713:
1699:
1671:
1668:
1665:
1661:
1651:
1624:
1621:
1610:
1609:
1608:
1592:
1588:
1564:
1561:
1556:
1552:
1548:
1545:
1542:
1537:
1533:
1509:
1497:
1495:
1493:
1488:
1485:
1480:
1478:
1473:
1469:
1455:
1446:
1444:
1428:
1425:
1422:
1413:
1411:
1382:
1379:
1376:
1373:
1370:
1366:
1362:
1357:
1354:
1351:
1347:
1334:
1331:
1326:
1316:
1313:
1301:
1299:
1289:
1284:
1281:
1278:
1274:
1268:
1263:
1258:
1255:
1252:
1249:
1246:
1242:
1236:
1224:
1221:
1209:
1203:
1190:
1186:
1182:
1179:
1176:
1171:
1168:
1165:
1162:
1159:
1155:
1151:
1146:
1143:
1140:
1137:
1134:
1130:
1117:
1114:
1105:
1103:
1086:
1083:
1077:
1071:
1068:
1065:
1062:
1059:
1055:
1051:
1046:
1043:
1040:
1037:
1034:
1030:
1022:
1015:
1011:
1005:
1002:
999:
994:
991:
988:
985:
982:
979:
976:
972:
965:
959:
956:
953:
949:
945:
940:
936:
932:
929:
926:
921:
918:
915:
912:
909:
905:
901:
896:
893:
890:
887:
884:
880:
863:
860:
855:
853:
843:
839:
833:
830:
827:
822:
819:
816:
813:
810:
807:
804:
800:
794:
791:
786:
784:
772:
769:
729:
726:
695:
692:
689:
686:
683:
679:
656:
653:
650:
646:
625:
622:
619:
599:
596:
593:
590:
587:
567:
540:
537:
519:
500:
496:
490:
485:
482:
479:
476:
473:
470:
467:
463:
457:
454:
449:
447:
437:
431:
427:
423:
420:
417:
412:
409:
406:
403:
400:
396:
392:
387:
384:
381:
378:
375:
371:
364:
362:
355:
320:
291:
269:
265:
261:
258:
255:
250:
246:
242:
237:
233:
212:
192:
171:
163:
159:
155:
147:
140:
138:
135:
130:
125:
121:
119:
115:
114:boxcar filter
111:
107:
104:
100:
96:
91:
89:
85:
81:
77:
73:
69:
65:
61:
57:
53:
43:
37:
33:
19:
7450:
7417:
7362:Mark Hulbert
7088:
6955:Morning star
6784:Market trend
6705:
6693:
6674:
6667:
6579:Econometrics
6529: /
6512:Chemometrics
6489:Epidemiology
6482: /
6455:Applications
6297:ARIMA model
6244:Q-statistic
6193:Stationarity
6089:Multivariate
6032: /
6028: /
6026:Multivariate
6024: /
5964: /
5960: /
5734:Bayes factor
5633:Signed rank
5545:
5519:
5511:
5499:
5194:Completeness
5030:Cohort study
4928:Opinion poll
4863:Missing data
4850:Study design
4805:Scatter plot
4727:Scatter plot
4720:Spearman's Ï
4682:Grouped data
4383:
4369:
4356:
4343:
4332:
4317:
4306:. Retrieved
4302:the original
4292:
4278:
4265:
4249:
4245:
4233:
4200:Rolling hash
4148:
4141:
4122:
4111:
4102:
3984:
3980:
3977:
3968:
3964:
3945:
3940:
3938:
3916:
3912:
3910:
3890:
3613:
3379:
3325:
3319:
3318:WMA weights
3103:
3072:
3068:
3064:
3062:
3058:pixelization
3051:
2641:
2424:
2352:
2346:
2342:
2337:
2333:
2331:
2196:
2070:
2062:
2058:
2056:
1877:
1691:
1522:environment
1501:
1489:
1483:
1481:
1476:
1474:
1470:
1447:
1414:
520:
157:
153:
151:
126:
122:
118:downsampling
113:
92:
72:rolling mean
71:
67:
63:
59:
55:
48:
7491:Time series
7405:forecasting
7357:John Murphy
7347:Charles Dow
7317:Ulcer index
7194:Force index
7164:Williams %R
7030:Pivot point
6915:Candlestick
6800:Candlestick
6707:WikiProject
6622:Cartography
6584:Jurimetrics
6536:Reliability
6267:Time domain
6246:(LjungâBox)
6168:Time-series
6046:Categorical
6030:Time-series
6022:Categorical
5957:(Bernoulli)
5792:Correlation
5772:Correlation
5568:JarqueâBera
5540:Chi-squared
5302:M-estimator
5255:Asymptotics
5199:Sufficiency
4966:Interaction
4878:Replication
4858:Effect size
4815:Violin plot
4795:Radar chart
4775:Forest plot
4765:Correlogram
4715:Kendall's Ï
3054:convolution
2679:results in
129:time series
95:convolution
68:moving mean
7480:Categories
7290:Arms index
7229:Volatility
7107:Trend line
7084:Mass index
7017:resistance
7006:Indicators
6830:Line break
6774:Dow theory
6574:Demography
6292:ARMA model
6097:Regression
5674:(Friedman)
5635:(Wilcoxon)
5573:Normality
5563:Lilliefors
5510:Student's
5386:Resampling
5260:Robustness
5248:divergence
5238:Efficiency
5176:(monotone)
5171:Likelihood
5088:Population
4921:Stratified
4873:Population
4692:Dependence
4648:Count data
4579:Percentile
4556:Dispersion
4489:Arithmetic
4424:Statistics
4308:2010-10-26
4226:References
4097:where the
3907:EWMA chart
97:. Thus in
84:cumulative
52:statistics
7342:Ned Davis
6992:Bear trap
6987:Bull trap
5955:Logistic
5722:posterior
5648:Rank sum
5396:Jackknife
5391:Bootstrap
5209:Bootstrap
5144:Parameter
5093:Statistic
4888:Statistic
4800:Run chart
4785:Pie chart
4780:Histogram
4770:Fan chart
4745:Bar chart
4627:L-moments
4514:Geometric
4071:−
4057:…
4046:−
4002:~
3858:⋯
3846:−
3817:Numerator
3769:−
3737:Numerator
3712:Numerator
3692:−
3681:−
3559:−
3545:⋯
3501:−
3490:−
3487:⋯
3484:−
3471:−
3330:equal to
3285:⋯
3273:−
3239:−
3202:−
3179:⋯
3168:−
3151:−
3086:−
3001:−
2922:−
2886:⋅
2827:⋅
2818:−
2743:⋅
2614:⋅
2608:−
2585:⋅
2516:⋯
2497:−
2472:⋯
2396:⋅
2374:⋯
2287:⋅
2160:⋯
2094:…
1999:ε
1996:⋅
1970:ε
1967:⋅
1932:ε
1886:ε
1878:A larger
1835:ε
1817:ε
1814:−
1800:∫
1796:⋅
1790:ε
1787:⋅
1772:↦
1752:→
1657:↦
1637:→
1565:ε
1546:ε
1543:−
1510:ε
1374:−
1363:−
1250:−
1237:−
1204:⏟
1180:⋯
1163:−
1138:−
1078:⏟
1063:−
1052:−
1038:−
986:−
973:∑
966:⏟
930:⋯
913:−
888:−
814:−
801:∑
687:−
591:−
477:−
464:∑
421:⋯
404:−
379:−
259:…
134:economics
7330:Analysts
7128:Momentum
7051:(A.D.X.)
6895:Triangle
6839:Patterns
6764:Breakout
6757:Concepts
6669:Category
6362:Survival
6239:Johansen
5962:Binomial
5917:Isotonic
5504:(normal)
5149:location
4956:Blocking
4911:Sampling
4790:QâQ plot
4755:Box plot
4737:Graphics
4632:Skewness
4622:Kurtosis
4594:Variance
4524:Heronian
4519:Harmonic
4153:See also
103:low-pass
88:weighted
76:averages
7407:methods
7276:Breadth
6942:Complex
6695:Commons
6642:Kriging
6527:Process
6484:studies
6343:Wavelet
6176:General
5343:Plug-in
5137:L space
4916:Cluster
4617:Moments
4435:Outline
4324:at the
3960:
3948:
3611:, then
2073:values
2067:average
1580:around
90:forms.
7292:(TRIN)
7175:Volume
7080:(MACD)
6924:Simple
6793:Charts
6564:Census
6154:Normal
6102:Manova
5922:Robust
5672:2-way
5664:1-way
5502:-test
5173:
4750:Biplot
4541:Median
4534:Lehmer
4476:Center
4256:
4099:median
4018:Median
80:simple
7305:Other
7286:(ADL)
7261:(VIX)
7239:(ATR)
7220:(VPT)
7214:(PCR)
7208:(OBV)
7202:(NVI)
7190:(EMV)
7155:(TSI)
7144:(RSI)
7138:(MFI)
7103:(SMI)
7097:(SAR)
7069:(KST)
7063:(DPO)
7057:(CCI)
7041:Trend
6847:Chart
6805:Renko
6188:Trend
5717:prior
5659:anova
5548:-test
5522:-test
5514:-test
5421:Power
5366:Pivot
5159:shape
5154:scale
4604:Shape
4584:Range
4529:Heinz
4504:Cubic
4440:Index
4142:In a
3983:over
3929:datum
3774:Total
3652:Total
3627:Total
3592:Total
2057:In a
86:, or
7245:(BB)
7196:(FI)
7166:(%R)
7119:(VI)
7112:Trix
7091:(MA)
7032:(PP)
6932:Doji
6815:Line
6810:Kagi
6421:Test
5621:Sign
5473:Wald
4546:Mode
4484:Mean
4254:ISBN
3415:and
3322:= 15
1935:>
1889:>
1321:prev
1229:prev
777:next
734:prev
545:next
162:mean
54:, a
34:and
7267:(Ï)
6875:Gap
5601:BIC
5596:AIC
3957:320
3951:ĂĂĂ
3911:An
3790:WMA
3582:by
3444:is
3425:WMA
3390:WMA
3113:WMA
2427:+ 1
1308:SMA
1216:SMA
764:SMA
721:SMA
612:to
532:SMA
350:SMA
315:SMA
158:SMA
70:or
66:or
62:or
50:In
7482::
4109:.
4009:SM
3007:CA
2967:CA
2928:CA
2892:CA
2833:CA
2749:CA
2694:CA
2653:CA
2620:CA
2591:CA
2402:CA
2345:=
2293:CA
2242:CA
2130:CA
2063:CA
1494:.
1445:.
749:.
120:.
82:,
7396:e
7389:t
7382:v
6742:e
6735:t
6728:v
5546:G
5520:F
5512:t
5500:Z
5219:V
5214:U
4416:e
4409:t
4402:v
4377:.
4311:.
4273:.
4103:n
4085:)
4080:1
4077:+
4074:n
4068:M
4064:p
4060:,
4054:,
4049:1
4043:M
4039:p
4035:,
4030:M
4026:p
4022:(
4014:=
3999:p
3985:n
3954:/
3870:1
3867:+
3864:2
3861:+
3855:+
3852:)
3849:1
3843:n
3840:(
3837:+
3834:n
3828:1
3825:+
3822:M
3810:=
3801:1
3798:+
3795:M
3779:M
3764:1
3761:+
3758:M
3754:p
3750:n
3747:+
3742:M
3732:=
3723:1
3720:+
3717:M
3701:1
3698:+
3695:n
3689:M
3685:p
3676:1
3673:+
3670:M
3666:p
3662:+
3657:M
3647:=
3638:1
3635:+
3632:M
3597:M
3568:1
3565:+
3562:n
3556:M
3552:p
3548:+
3542:+
3537:M
3533:p
3510:1
3507:+
3504:n
3498:M
3494:p
3479:M
3475:p
3466:1
3463:+
3460:M
3456:p
3452:n
3430:M
3401:1
3398:+
3395:M
3365:.
3360:2
3356:)
3353:1
3350:+
3347:n
3344:(
3341:n
3320:n
3297:1
3294:+
3291:2
3288:+
3282:+
3279:)
3276:1
3270:n
3267:(
3264:+
3261:n
3254:)
3251:1
3248:+
3245:)
3242:n
3236:M
3233:(
3230:(
3226:p
3222:+
3217:)
3214:2
3211:+
3208:)
3205:n
3199:M
3196:(
3193:(
3189:p
3185:2
3182:+
3176:+
3171:1
3165:M
3161:p
3157:)
3154:1
3148:n
3145:(
3142:+
3137:M
3133:p
3129:n
3123:=
3118:M
3089:1
3083:n
3073:n
3069:n
3026:1
3023:+
3020:n
3013:n
2996:1
2993:+
2990:n
2986:x
2979:+
2973:n
2960:=
2947:1
2944:+
2941:n
2934:n
2917:1
2914:+
2911:n
2907:x
2903:+
2898:n
2883:)
2880:1
2877:+
2874:n
2871:(
2865:=
2852:1
2849:+
2846:n
2839:n
2824:)
2821:1
2815:1
2812:+
2809:n
2806:(
2803:+
2798:1
2795:+
2792:n
2788:x
2781:=
2768:1
2765:+
2762:n
2755:n
2740:n
2737:+
2732:1
2729:+
2726:n
2722:x
2715:=
2706:1
2703:+
2700:n
2665:1
2662:+
2659:n
2626:n
2611:n
2603:1
2600:+
2597:n
2582:)
2579:1
2576:+
2573:n
2570:(
2567:=
2562:1
2559:+
2556:n
2552:x
2532:)
2527:n
2523:x
2519:+
2513:+
2508:1
2504:x
2500:(
2494:)
2489:1
2486:+
2483:n
2479:x
2475:+
2469:+
2464:1
2460:x
2456:(
2453:=
2448:1
2445:+
2442:n
2438:x
2425:n
2408:n
2393:n
2390:=
2385:n
2381:x
2377:+
2371:+
2366:1
2362:x
2347:N
2343:n
2338:n
2334:n
2318:.
2312:1
2309:+
2306:n
2299:n
2284:n
2281:+
2276:1
2273:+
2270:n
2266:x
2259:=
2254:1
2251:+
2248:n
2217:1
2214:+
2211:n
2207:x
2183:.
2177:n
2171:n
2167:x
2163:+
2157:+
2152:1
2148:x
2141:=
2136:n
2105:n
2101:x
2097:,
2091:.
2086:1
2082:x
2071:n
2061:(
1993:2
1964:2
1960:1
1938:0
1912:f
1892:0
1858:t
1855:d
1850:)
1847:t
1844:(
1840:f
1832:+
1827:o
1823:x
1809:o
1805:x
1784:2
1780:1
1767:x
1758:R
1746:R
1740::
1735:f
1731:A
1727:M
1700:f
1672:)
1669:x
1666:(
1662:f
1652:x
1643:R
1631:R
1625::
1622:f
1593:o
1589:x
1568:]
1562:+
1557:o
1553:x
1549:,
1538:o
1534:x
1530:[
1456:k
1429:n
1426:=
1423:k
1390:)
1383:1
1380:+
1377:k
1371:n
1367:p
1358:1
1355:+
1352:n
1348:p
1342:(
1335:k
1332:1
1327:+
1317:,
1314:k
1302:=
1290:k
1285:1
1282:+
1279:n
1275:p
1269:+
1264:k
1259:1
1256:+
1253:k
1247:n
1243:p
1225:,
1222:k
1210:=
1198:)
1191:n
1187:p
1183:+
1177:+
1172:2
1169:+
1166:k
1160:n
1156:p
1152:+
1147:1
1144:+
1141:k
1135:n
1131:p
1125:(
1118:k
1115:1
1106:=
1094:)
1087:0
1084:=
1072:1
1069:+
1066:k
1060:n
1056:p
1047:1
1044:+
1041:k
1035:n
1031:p
1023:+
1016:i
1012:p
1006:1
1003:+
1000:n
995:2
992:+
989:k
983:n
980:=
977:i
960:1
957:+
954:n
950:p
946:+
941:n
937:p
933:+
927:+
922:3
919:+
916:k
910:n
906:p
902:+
897:2
894:+
891:k
885:n
881:p
871:(
864:k
861:1
856:=
844:i
840:p
834:1
831:+
828:n
823:2
820:+
817:k
811:n
808:=
805:i
795:k
792:1
787:=
773:,
770:k
730:,
727:k
696:1
693:+
690:k
684:n
680:p
657:1
654:+
651:n
647:p
626:1
623:+
620:n
600:2
597:+
594:k
588:n
568:k
541:,
538:k
501:i
497:p
491:n
486:1
483:+
480:k
474:n
471:=
468:i
458:k
455:1
450:=
438:k
432:n
428:p
424:+
418:+
413:2
410:+
407:k
401:n
397:p
393:+
388:1
385:+
382:k
376:n
372:p
365:=
356:k
321:k
292:k
270:n
266:p
262:,
256:,
251:2
247:p
243:,
238:1
234:p
213:n
193:k
172:k
156:(
58:(
38:.
20:)
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