Multivariate Time series using Vector Autoregression (VAR)
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- เผยแพร่เมื่อ 5 ก.ย. 2024
- #datascience #machinelearning #timeseries
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Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time
VAR models (vector autoregressive models) are used for multivariate time series. The structure is that each variable is a linear function of past lags of itself and past lags of the other variables
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another
The Augmented Dickey Fuller Test (ADF) is unit root test for stationarity. Unit roots can cause unpredictable results in your time series analysis.