Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports univariate time-series data with a seasonal component. It adds three new hyperparameters to specify the autoregression (AR), differencing (I) and moving average (MA) for the seasonal component of the series, as well as an additional parameter for the period of the seasonality.
We checked for possible combinations of p,d,q,m for a given time series
After basic computations for the series, we got 0,1,0,12 as the p,d,q,m values for the given series. The result was 38.56 Root Mean Square Error and the visualization for the prediction for the upcoming year can be seen below:
For a better understanding of SARIMAX refer: https://machinelearningmastery.com/sarima-for-time-series-forecasting-in-python/
We checked for possible combinations of p,d,q,m for a given time series
After basic computations for the series, we got 0,1,0,12 as the p,d,q,m values for the given series. The result was 38.56 Root Mean Square Error and the visualization for the prediction for the upcoming year can be seen below:
For a better understanding of SARIMAX refer: https://machinelearningmastery.com/sarima-for-time-series-forecasting-in-python/
No comments:
Post a Comment