In the last column of this table, a moving average of order 5 is shown, providing an estimate of the trend-cycle. Table 6.1: Annual electricity sales to residential customers in South Australia.

12.9 Dealing with missing values and outliersįigure 6.4: Residential electricity sales (excluding hot water) for South Australia: 1989–2008.įor example, consider Figure 6.4 which shows the volume of electricity sold to residential customers in South Australia each year from 1989 to 2008 (hot water sales have been excluded).

12.8 Forecasting on training and test sets.

12.7 Very long and very short time series.

12.5 Prediction intervals for aggregates.

12.3 Ensuring forecasts stay within limits.

10.7 The optimal reconciliation approach.

10 Forecasting hierarchical or grouped time series.

9.4 Stochastic and deterministic trends.

7.5 Innovations state space models for exponential smoothing.

7.4 A taxonomy of exponential smoothing methods.

6.7 Measuring strength of trend and seasonality.

5.9 Correlation, causation and forecasting.

1.7 The statistical forecasting perspective.

1.6 The basic steps in a forecasting task.

Double moving average eviews 9