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kjetil b halvorsen ♦
kjetil b halvorsen ♦
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After I fit a sarima model with some historical sales data (for example A dataset), I get coefficients of sma1 and ar1. And I'd like to apply this model to current sales data (for example B dataset) and forecast future sales. That's where my concerns comes up, it seem like neither sarima.for() nor predict() or any other forecasting functions has the 'new.data' argument (I don't mean xreg, so ignore xreg here, I only mean the univariate series itself). Since for general regression models, we can store the coefficient and apply the model to future testing dataset. Does that mean we cannot store the coefficient from the ARIMA model and apply the model to other period of data? If time series works differently, what should I do if I want use the model I get? More specifically, when we say a time series model, do we only mean the p, d, q that we have get, how about the coefficient? Correct me if I'm wrong. Looking forward to any enlightenment! Thanks!

After I fit a sarima model with some historical sales data (for example A dataset), I get coefficients of sma1 and ar1. And I'd like to apply this model to current sales data (for example B dataset) and forecast future sales. That's where my concerns comes up, it seem like neither sarima.for() nor predict() or any other forecasting functions has the 'new.data' argument (I don't mean xreg, so ignore xreg here, I only mean the univariate series itself). Since for general regression models, we can store the coefficient and apply the model to future testing dataset. Does that mean we cannot store the coefficient from the ARIMA model and apply the model to other period of data? If time series works differently, what should I do if I want use the model I get? More specifically, when we say a time series model, do we only mean the p, d, q that we have get, how about the coefficient? Correct me if I'm wrong. Looking forward to any enlightenment! Thanks!

After I fit a sarima model with some historical sales data (for example A dataset), I get coefficients of sma1 and ar1. And I'd like to apply this model to current sales data (for example B dataset) and forecast future sales. That's where my concerns comes up, it seem like neither sarima.for() nor predict() or any other forecasting functions has the 'new.data' argument (I don't mean xreg, so ignore xreg here, I only mean the univariate series itself). Since for general regression models, we can store the coefficient and apply the model to future testing dataset. Does that mean we cannot store the coefficient from the ARIMA model and apply the model to other period of data? If time series works differently, what should I do if I want use the model I get? More specifically, when we say a time series model, do we only mean the p, d, q that we have get, how about the coefficient? Correct me if I'm wrong. Looking forward to any enlightenment!

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EmLp
EmLp
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Can I apply ARIMA(p, d, q) model to testing dataset and make forecast with the testing dataset? Just like the scenario of regression model?

After I fit a sarima model with some historical sales data (for example A dataset), I get coefficients of sma1 and ar1. And I'd like to apply this model to current sales data (for example B dataset) and forecast future sales. That's where my concerns comes up, it seem like neither sarima.for() nor predict() or any other forecasting functions has the 'new.data' argument (I don't mean xreg, so ignore xreg here, I only mean the univariate series itself). Since for general regression models, we can store the coefficient and apply the model to future testing dataset. Does that mean we cannot store the coefficient from the ARIMA model and apply the model to other period of data? If time series works differently, what should I do if I want use the model I get? More specifically, when we say a time series model, do we only mean the p, d, q that we have get, how about the coefficient? Correct me if I'm wrong. Looking forward to any enlightenment! Thanks!