Methods of Forecasting
Time Series: Time series analysis comprises methods that attempt to understand such time series, often either to understand the underlying theory of the data points (where did they come from? what generated them?), or to make forecasts (predictions).
Moving Average: This simplest forecasting method is the moving average forecast. The method simply averages of the last m observations. It is useful for time series with a slowly changing mean.
Exponential Smoothing This method considers the entire past in its’ forecast, but weighs recent experience more heavily than less recent. The computations are simple because only the estimate of the previous period and the current data determine the new estimate. The method is useful for time series with a slowly changing mean.
Regression The moving average method does not respond well to a time series that increases or
decreases with time. Here we include a linear trend term in the model. The regression method approximates the model by constructing a linear equation that provides the least squares fit to the last m observations.
Seasonality We model seasonality with a multiplicative seasonal index. The data is adjusted by dividing by the index and the adjusted data is used to obtain forecasts using one of the methods above.
0 Comments