Autocorrelation

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Autocorrelation

The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation.

Serial Correlation

In technical analysis, a measure of how well past occurrences predict future occurrences. Most importantly, serial correlation checks whether and how often a particular price movement will result in a different price movement. Serial correlation lies at the heart of technical analysis. It is also called autocorrelation.
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Initially suggested by Von Storch and Navarra (1995), the MK_prew removes a serial correlation component, such as the lag-1 auto-correlation coefficient, from the dataset prior to the trend analysis (BURN; ELNUR, 2002; YUE et al.
An approach derived from white noise analysis is bispectral analysis (Marmarelis and Sheby, 1988; Jouny and Walton, 1991), which involves triple auto-correlations and can detect return signals with skewed amplitude probability distributions.
Thus, the computation of auto-correlation and cross-correlation of the output process becomes very important for further analysis of the system.
12 shows that in this regime a strong auto-correlation prevails until the bit 600 approximately.
As a result, propagation is also weaker, with observed TFP auto-correlation (0.
Therefore, it is necessary to select the correct statistical amount to analyze the spatial auto-correlation according to the different types of the spatial data's observed values.
By using the periodic statistics such as mean and auto-correlation of the primary waveform, CR can detect a random signal with a specific modulation type in the presence of random stochastic noise.
Durbin-Watson statistic has been used to analyse the problem of auto-correlation.
Appropriate ARIMA models were fitted after judging the time-series data for stationarity based on visual inspection, auto-correlation function and partial auto-correlation function.
Then, this signal is used to obtain the auto-correlation function (AKF), submitted to analysis block to extract correlation properties discussed in section II and is stored in codes data base if it produces better correlation parameter than previous signal.
The discussion for cross auto-correlation pattern of stock returns started after Lo and Mackinlay (1990) who proved in their seminal paper that the size of the firm is a major determinant of lead-lag pattern.

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