How is M chosen in moving average forecasting?

• A. By trial and error, tested on historical data
• B. Fixed by the analyst before data is examined
• C. Set to a standard value for all datasets
• D. Selected randomly for each forecast

Answer: (A)

Choosing the window length M in moving average forecasting is about finding the right amount of smoothing for the data. You try several window sizes (for example, 3, 6, or 12 periods) and assess how well the forecasts match historical outcomes, usually using a validation set or cross-validation and a forecast-error measure like RMSE or MAE. The value of M that gives the smallest error on unseen data is kept. This empirical approach makes sense because the ideal smoothing depends on the data’s noise level and any underlying patterns, so what works for one series might not work for another. Too small a window makes forecasts noisy and overly responsive; too large a window smooths out real changes and slows reaction to shifts. That’s why M is chosen by testing different values against historical data. It’s not fixed in advance for all datasets, nor chosen randomly, and it shouldn’t be assumed to be the same for every case.