Time series refers to a sequence of observations following each other in time, where adjacent observations are correlated. This can be used to model, simulate, and forecast behavior for a system. Time series models are frequently used in fields such as economics, finance, biology, and engineering.
Mathematica provides a full suite of time series functionality, including standard models such as MA, AR, and ARMA, as well as several extensions. Time series models can be simulated, estimated from data, and used to produce forecasts of future behavior.
MAProcess — moving-average process (scalar and vector)
ARProcess — autoregressive process (scalar and vector)
ARMAProcess — autoregressive moving-average process (scalar and vector)
SARIMAProcess — seasonal integrating ARMA for polynomial and periodic trends
RandomFunction — simulate a time series process
EstimatedProcess — estimate parameters in a time series process
TimeSeriesForecast — forecast future values in a time series process
TemporalData — time series data
WeakStationarity — conditions for a time series model to be weakly stationary
TimeSeriesInvertibility — conditions for a time series model to be invertible
ToInvertibleTimeSeries — gives an invertible representation of the time series
UnitRootTest — test whether time series data is stationary
Differences — detrending and deseasoning data
MovingAverage — moving-average filtering