What are the forecasting models that can be used for stationary time series data?
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Show Principles and risks of forecasting (pdf) Famous forecasting quotes Stationarity and differencing Statistical stationarity Statistical stationarity: A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. Most statistical forecasting methods are based on the assumption that the time series can be rendered approximately stationary (i.e., "stationarized") through the use of mathematical transformations. A stationarized series is relatively easy to predict: you simply predict that its statistical properties will be the same in the future as they have been in the past! (Recall our famous forecasting quotes.) The predictions for the stationarized series can then be "untransformed," by reversing whatever mathematical transformations were previously used, to obtain predictions for the original series. (The details are normally taken care of by your software.) Thus, finding the sequence of transformations needed to stationarize a time series often provides important clues in the search for an appropriate forecasting model. Stationarizing a time series through differencing (where needed) is an important part of the process of fitting an ARIMA model, as discussed in the ARIMA pages of these notes. Another reason for trying to stationarize a time series is to be able to obtain meaningful sample statistics such as means, variances, and correlations with other variables. Such statistics are useful as descriptors of future behavior only if the series is stationary. For example, if the series is consistently increasing over time, the sample mean and variance will grow with the size of the sample, and they will always underestimate the mean and variance in future periods. And if the mean and variance of a series are not well-defined, then neither are its correlations with other variables. For this reason you should be cautious about trying to extrapolate regression models fitted to nonstationary data. Most
business and economic time series are far from stationary when expressed in their original units of measurement, and even after deflation or seasonal adjustment they will typically still exhibit trends, cycles, random-walking, and other non-stationary behavior. If the series has a stable long-run trend and tends to revert to the trend line following a disturbance, it may be possible to stationarize it by de-trending (e.g., by fitting a trend line and subtracting it out prior to
fitting a model, or else by including the time index as an independent variable in a regression or ARIMA model), perhaps in conjunction with logging or deflating. Such a series is said to be trend-stationary. However, sometimes even de-trending is not sufficient to make the series stationary, in which case it may be necessary to transform it into a series of period-to-period and/or season-to-season differences. If the mean, variance, and autocorrelations
of the original series are not constant in time, even after detrending, perhaps the statistics of the changes in the series between periods or between seasons will be constant. Such a series is said to be difference-stationary. (Sometimes it can be hard to tell the difference between a series that is trend-stationary and one that is difference-stationary, and a so-called unit root testmay be used to get a more definitive answer.
We will return to this topic later in the course.) The first difference of a time series is the series of changes from one period to the next. If Yt denotes the value of the time series Y at period t, then the first difference of Y at period t is equal to Yt-Yt-1. In Statgraphics, the first difference of Y is expressed as DIFF(Y), and in RegressIt it is Y_DIFF1. If the first difference of Y is stationary and also completely random (not autocorrelated), then Y is described by a random walk model: each value is a random step away from the previous value. If the first difference of Y is stationary but not completely random--i.e., if its value at period t is autocorrelated with its value at earlier periods--then a more sophisticated forecasting model such as exponential smoothing or ARIMA may be appropriate. (Note: if DIFF(Y) is stationary and random, this indicates that a random walk model is appropriate for the original series Y, not that a random walk model should be fitted to DIFF(Y). Fitting a random walk model to Y is logically equivalent to fitting a mean (constant-only) model to DIFF(Y).) Here is a graph of the first difference of AUTOSALE/CPI, the deflated auto sales series. Notice that it now looks approximately stationary (at least the mean and variance are more-or-less constant) but it is not at all random (a strong seasonal pattern remains):
The following spreadsheet illustrates how the first difference is calculated for the deflated auto sales data:
Go on to next topic: The logarithm transformation What forecasting methods work best for stationary data?A moving average (MA) is a type of model used for time-series forecasting. The moving average models are primarily used for stationary data, the data where we don't see significant trends or seasonality.
What models are used for time series forecasting?ARIMA and SARIMA
AutoRegressive Integrated Moving Average (ARIMA) models are among the most widely used time series forecasting techniques: In an Autoregressive model, the forecasts correspond to a linear combination of past values of the variable.
What are the 4 types of forecasting model?Four common types of forecasting models. Time series model.. Econometric model.. Judgmental forecasting model.. The Delphi method.. What is stationary in time series forecasting?A stationary time series is one whose properties do not depend on the time at which the series is observed. Thus, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different times.
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