What Is Time Series Regression?
Time series regression is a statistical method for predicting a future response based on the response history (known as autoregressive dynamics) and the transfer of dynamics from relevant predictors. Time series regression can help you understand and predict the behavior of dynamic systems from experimental or observational data. Common uses of time series regression include modeling and forecasting of economic, financial, biological, and engineering systems.
You can start a time series analysis by building a design matrix (
to get an estimate of a linear relationship of the response
Typically, time series modeling involves picking a model structure (such as an ARMA form or a transfer function) and incorporating known attributes of the system such as non-stationarities. Some examples are:
- Autoregressive integrated moving average with exogenous predictors (ARIMAX)
- Distributed lag models (transfer functions)
- State space models
- Spectral models
- Nonlinear ARX models
The choice of model depends on your goals for the analysis and the properties of the data. See Econometrics Toolbox™ and System Identification Toolbox™ for more details.
Examples and How To
Economic and Financial Systems
Engineering and Biological Systems
Software Reference
See also: cointegration, GARCH models, DSGE, equity trading, predictive modeling, time series analysis