averaging prior observations in a time series affects the forecast
and the accuracy statistics.
Averaging prior observations is often a good way to forecast
future observations for relatively stable time series. In the
graph, the red dots with solid lines represent the time series data
and the blue dots with dotted lines represent the moving average
forecast.
Use the buttons at the bottom to select different moving average
methods for the prior three observations. The "Naive Forecast" uses
only the immediate prior observation. The "Moving Average" gives
equal weight to the three prior observations. The "Weighted Moving
Average" weights observations by recency. Compare how the different
weighting systems perform as forecasts, both visually and in terms
of the statistics, MAE (mean absolute error), MSE (mean squared
error), and MAPE (mean absolute percentage error).
- Conceptual Overview Explore How Using Different Weights For Averaging Prior Observations In A Time Series Affects The F 1 (31.7 KiB) Viewed 31 times
- Conceptual Overview Explore How Using Different Weights For Averaging Prior Observations In A Time Series Affects The F 2 (22.37 KiB) Viewed 31 times
1. Out of the three methods, "Naive Forecast", "Moving Average", and "Weighted Moving Average", which produces a smaller mean absolute error? a. Naive Forecast b. Moving Average c. Weighted Moving Average d. None of the above a 2.Which method produces the smallest mean squared error? a. Naive Forecast b. Moving Average c. Weighted Moving Average d. None of the above с