IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Applications of time series models
A time series analyst uses simple exponential smoothing, which corresponds to an ARIMA(0,1,1) model, with smoothing parameter 0.3. The previous smoothed forecast for period t was 100 and the actual observation was 120. What is the forecast for period t+1?
The forecast for the next period is 106. Exponential smoothing updates the previous forecast of 100 by 0.3 times the forecast error of 20. Using 0.7 instead of 0.3 would wrongly give 114.
- A106Correct
- B114
- C110
- D100
- 120
Explanation
New forecast = old forecast + alpha x (actual - old forecast) = 100 + 0.3 x 20 = 106. Using 0.7 as the weight gives 114, which is the mistake of swapping alpha and 1 - alpha.
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