Question

The time series forecasting method that gives equal weightage to each of the m most recent observations is

1. Moving average method
2. Exponential smoothing with linear trend
3. Triple Exponential smoothing
4. Kalman Filter

Answer 1

Correct Answer - Option 1 : Moving average method

Explanation:

The moving average method uses the average of the most recent n data values in the time series as the forecast for the next period.

\({F_{t + 1}} = \frac{{{D_t} + {D_{t - 1}} + \ldots + {D_{t - n + 1}}}}{n}\)

Note that the n past observations are equally weighted.

Issues with moving average forecast:

• All n past observations treated equally
• Observations older than n are not included at all

Weighted Moving Average

A weighted moving average allows any weights to be placed on each element, providing of course, that the sum of all weights equals one.

Exponential Smoothing:

In this approach, the most recent past period demand is weighted most heavily. In a continuing manner the weights assigned to successively past period demands decrease according to exponential law.

F_t+1 = αD_t + (1 – α) F_t