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 ?
Gradient radial basis function networks for nonlinear and nonstationary time series prediction.
Application of chaos and fractal models to water quality time series prediction.
Tang, "An EMD-SVR method for non-stationary time series prediction," in Proceedings of the International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 13), pp.
A New Algorithm for Time Series Prediction by Temporal Fuzzy Clustering.
Time series prediction can be seen as the task of finding regularities and dependencies in the data set, and NNs can be taught to emulate the underlying dynamics of the system.
We distinguished three types' economic applications of neural networks: Classification of economic agents, time series prediction and the modeling of bounded rational agents.
But the chaotic time series prediction studied in this paper has a completely different mechanism.
This builds an internal memory for time series prediction [3][8].
For this purpose, many data-driven models have been developed, including linear, nonlinear, parametric and nonparametric models for hydrologic time series prediction in the past decades (Marques et al.
At present, the mainstream prediction methods adopted by researchers and enterprises in the industry on supply chain demand prediction are roughly categorized as two categories: one is single prediction method, such as adopting neural network prediction method, grey prediction method, markov prediction method, time series prediction method, prediction method based on quantity of value; the other is combination prediction method, i.
The paper deals with the utilization of time series prediction for control of technological process in real time.
A variety of traditional time series prediction approaches have already been proposed for this problem, such as fuzzy rule [2], Kalman filter [3], grey prediction [4], ARMA [5], and multiple regression [6].