# 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 ?
The prediction results are compared with the time series prediction method and the least squares prediction method to verify its rationality.
It is known that SNPOM is an efficient optimization method fallen into this category, which makes good use of the model parameters feature and gives impressive performance in time series prediction and nonlinear control.
Application of chaos and fractal models to water quality time series prediction. Environmental Modelling & Software, 24(5), 632-636, DOI: 10.1016/j.envsoft.2008.10.004
Because the power transmission line icing is a multivariate time series described by formula (6), according to the phase-space reconstruction model shown as formula (7), the time series prediction model of power transmission line icing can be found as the following formula:
Time Series Prediction. Time series forecasting is an important subject in which past observations of an interested variable are recorded and analyzed to establish a prediction model [24].
This is in contrast to many traditional techniques for time series predictions, such as ARIMA, which assume that the series are generated from linear processes and as a result might be inappropriate for most real-world problems that are nonlinear [5, 6].
A new form of RNN training methods, echo state network (ESN), has been proposed by Jaeger and Haas [1], which is simple and applicable for time series prediction with high accuracy and computational efficiency.
There has been a great effort in the research community to the development and improvement of the time series prediction models.
So it is a new way for nonlinear time series prediction. Su et al.
DAN2 for time series prediction is described by equations (3), (4) and (6).
De Gooijer and Hyndman (2006) studied a method for time series prediction and concluded that the method was needed to be proved in some aspects.
Time series prediction of local weather is crucial for many aspects of energy conservation, economic operation, and improved thermal comfort in commercial buildings.

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