![]() Finally, the time series model may give more accurate forecasts than an explanatory or mixed model. Third, the main concern may be only to predict what will happen, not to know why it happens. Second, it is necessary to know or forecast the future values of the various predictors in order to be able to forecast the variable of interest, and this may be too difficult. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behaviour. However, there are several reasons a forecaster might select a time series model rather than an explanatory or mixed model. These models are discussed in Chapter 9.Īn explanatory model is useful because it incorporates information about other variables, rather than only historical values of the variable to be forecast. They are known as dynamic regression models, panel data models, longitudinal models, transfer function models, and linear system models (assuming that \(f\) is linear). These types of “mixed models” have been given various names in different disciplines. A model with predictor variables might be of the form For example, suppose we wish to forecast the hourly electricity demand (ED) of a hot region during the summer period. Predictor variables are often useful in time series forecasting. ![]() Predictor variables and time series forecasting These models are discussed in Chapters 6, 7 and 8, respectively. Time series models used for forecasting include decomposition models, exponential smoothing models and ARIMA models. Therefore they will extrapolate trend and seasonal patterns, but they ignore all other information such as marketing initiatives, competitor activity, changes in economic conditions, and so on. The simplest time series forecasting methods use only information on the variable to be forecast, and make no attempt to discover the factors that affect its behaviour. In this case the forecasts are expected to be accurate, and hence the prediction intervals are quite narrow. These prediction intervals are a useful way of displaying the uncertainty in forecasts. The light shaded region shows 95% prediction intervals. That is, each future value is expected to lie in the dark shaded region with a probability of 80%. The dark shaded region shows 80% prediction intervals. Notice how the forecasts have captured the seasonal pattern seen in the historical data and replicated it for the next two years. The blue lines show forecasts for the next two years. In this book we are concerned with forecasting future data, and we concentrate on the time series domain.įigure 1.1: Australian quarterly beer production: 1992Q1–2010Q2, with two years of forecasts. Most quantitative prediction problems use either time series data (collected at regular intervals over time) or cross-sectional data (collected at a single point in time). Each method has its own properties, accuracies, and costs that must be considered when choosing a specific method. There is a wide range of quantitative forecasting methods, often developed within specific disciplines for specific purposes. it is reasonable to assume that some aspects of the past patterns will continue into the future.numerical information about the past is available.Quantitative forecasting can be applied when two conditions are satisfied: These methods are discussed in Chapter 4. These methods are not purely guesswork-there are well-developed structured approaches to obtaining good forecasts without using historical data. If there are no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. The appropriate forecasting methods depend largely on what data are available. 12.9 Dealing with missing values and outliers.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.
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