Contents
How is forecast uncertainty calculated?
For short-term models, the uncertainty of point estimates can be measured by the mean squared error, in which forecasted values are compared with actual observations. Other measures of uncertainty include the confidence interval and mean absolute percentage error.
What is alpha in arima model?
The alpha argument on the conf_int() function on the PredictionResult specifies the prediction level. An alpha of 0.05 means that the ARIMA model will estimate the upper and lower values around the forecast where there is a only a 5% chance that the real value will not be in that range.
What is uncertainty in demand forecasting?
It refers to the process of predicting the future demand for the firm’s product. Demand forecasts are subject to error and uncertainty, which arise from three principal sources: 1) Data about past and present market, 2) Methods of forecasting, and, 3) Environmental change.
How to understand time series forecast uncertainty using…?
Running the example prints the forecasts and prediction intervals for each alpha value. We can see that we get the same forecast value each time and an interval that expands as our desire for a ‘safer’ interval increases. We can see that an 80% captures our actual value just fine in this specific case.
How does a time series forecast model work?
Time series forecast models can both make predictions and provide a prediction interval for those predictions. Prediction intervals provide an upper and lower expectation for the real observation.
How to calculate prediction intervals for time series?
Extending the example above, we can report our forecast with a few different commonly used prediction intervals of 80%, 90%, 95% and 99%. The complete example is listed below. Running the example prints the forecasts and prediction intervals for each alpha value.
Why is stationarity important in time series analysis?
The reason stationarity has been mentioned is due to its key importance in time series analysis, a series must be classed as stationary before any time series model can be fitted to it. It is often the case with real world data that series are non-stationary and so a set of mathematical techniques have been devised to convert these to stationary.