Forecasting with Exponential Smoothing: How to Improve Accuracy
How is forecast calculated using exponential smoothing?
Based on the data provided, what is the forecast for periods 11 and 12?
Forecast Calculation using Exponential Smoothing
Exponential smoothing is a popular time series forecasting method that calculates the forecast for the next period as a weighted average of the previous forecast and the actual value for the current period. The weight given to the previous forecast decreases exponentially, while the weight given to the actual value increases.
Forecast for Periods 11 and 12
With α=0.1 and the initial forecast for October of $1.81, the forecast for period 11 is $1.82 and the forecast for period 12 is $1.83. The forecasts are slightly lower than the actual values for those periods due to the relatively small smoothing constant, α.
Exponential smoothing is an effective method for forecasting time series data by adjusting the previous forecast based on new data. By using a smoothing constant, α, the model can adapt to changes in the actual values over time.
The forecast for each period is calculated by multiplying the previous forecast by (1-α) and the actual value by α, then summing the results. This process continues for each forecast period, with the weight given to the previous forecast gradually decreasing.
In this case, with α=0.1, the forecast for periods 11 and 12 were calculated to be slightly lower than the actual values. This is due to the small smoothing constant, which makes the forecasts less responsive to changes in the data.
To improve the accuracy of the forecasts, one could consider adjusting the smoothing constant α. A larger α would make the forecasts more responsive to changes in the actual values, potentially providing more accurate predictions for future periods.