time series

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Time Series

A comparison of a variable to itself over time. One of the most common time series, especially in technical analysis, is a comparison of prices over time. For example, one may compile a time series of a security over the course of a week or a month or a year, and then use it in the determination of future price movements.

time series

A set of variables with values related to the respective times the variables are measured. Thus, a weekly record of a stock's price throughout a period of years is a time series. Time series are often used to project future values by observing how the value of a variable has changed in the past.

time series

any statistical information recorded over successive time periods. See TIME-SERIES ANALYSIS.
References in periodicals archive ?
Repeating the analysis in Section VA using this parsimonious multivariate time series model (as opposed to a univariate time series model), it is found that no breaks occur for any of the four series (FS, NFS, FH, and NFH).
All the univariate time series of survival rate indices contained significant positive first-order autoregressive parameters (see Table 2).
Figure 2 shows the RMSFE values(4) for the univariate time series models and the NZIER forecasts(5) over horizons up to eight quarters ahead.
Dennis and Taper (1994) reviewed many of the statistical and conceptual problems associated with the detection of density dependence in single populations, and proposed statistical methods for analyzing univariate time series data with a stochastic model of density-dependent population growth.
Univariate time series models are used, either explicitly or implicitly, to forecast the expected bond default risk premium.
Univariate Time Series Model of Quarterly Accounting Earnings per Share: A Proposed Model, Journal of Accounting Research, (Spring): 179-189.
It is well known in the literature that traditional tests for unit roots in univariate time series, such as the Dickey-Fuller [5; 6; 8] and Phillips-Perron [14; 16] tests, have very low power against local stationary alternative.
The conclusion is that X-11 filtering tends to scramble some univariate time series properties of the data.
Traditional univariate time series models, such as Auto-Regression (AR), Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA) [3], only consider the historical data of the target serie alone.