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Richard Hardy
Richard Hardy
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In a timeseriestime series forecasting, should we apply differencing on entire dataset if one or two features are non stationary?

I'm working on a timeseriestime series forecasting model using VAR (Vector Autoregression). I have 6 features, out of which 2 features are not stationary,. If I apply first order-order differencing on those features, they are stationary. Should I apply differencing on entire dataset or should I apply on only those 2 features?

In a timeseries forecasting, should we apply differencing on entire dataset if one or two features are non stationary?

I'm working on a timeseries forecasting model using VAR (Vector Autoregression). I have 6 features, out of which 2 features are not stationary, If I apply first order differencing on those features, they are stationary. Should I apply differencing on entire dataset or should I apply on only those 2 features?

In a time series forecasting, should we apply differencing on entire dataset if one or two features are non stationary?

I'm working on a time series forecasting model using VAR (Vector Autoregression). I have 6 features, out of which 2 features are not stationary. If I apply first-order differencing on those features, they are stationary. Should I apply differencing on entire dataset or should I apply on only those 2 features?

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MasterBlasterCoder
MasterBlasterCoder
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In a timeseries forecasting, should we apply differencing on entire dataset if one or two features are non stationary?

I'm working on a timeseries forecasting model using VAR (Vector Autoregression). I have 6 features, out of which 2 features are not stationary, If I apply first order differencing on those features, they are stationary. Should I apply differencing on entire dataset or should I apply on only those 2 features?