Contents
How are different models used to forecast time series?
The situation, in more detail, is this: I am using several different models (including SVM and a few others) to forecast a time series. My models are based on differenced data since the original data is not stationary. Now I have a vector of predicted values for each model, but all the forecasts are for the differenced data.
How are seasonally differenced data used in forecasting?
If seasonally differenced data appear to be white noise, then an appropriate model for the original data is yt = yt−m +εt. y t = y t − m + ε t. Forecasts from this model are equal to the last observation from the relevant season.
Which is the difference between the original and differenced series?
The differenced series is the change between consecutive observations in the original series, and can be written as y′ t = yt −yt−1. y t ′ = y t − y t − 1. The differenced series will have only T −1 T − 1 values, since it is not possible to calculate a difference y′ 1 y 1 ′ for the first observation.
How to convert the differenced forecast to the original?
If z are the differenced data and y the original, then: and so on. The cumulative sums of the z ^ values are easy, and adding y t is also easy.
Which is the first difference in a time series?
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.
Can a time series forecast be cross validated?
In some circumstances the parameter updates can be computed in terms of the old estimates and the data (e.g. state-space models), but for many models you just recompute everything like you did with the previous forecasts. Thanks for contributing an answer to Cross Validated!
How is a stationarized time series easy to predict?
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 .)
How to judge the accuracy of time series forecasts?
The commonly used accuracy metrics to judge forecasts are: 1 Mean Absolute Percentage Error (MAPE) 2 Mean Error (ME) 3 Mean Absolute Error (MAE) 4 Mean Percentage Error (MPE) 5 Root Mean Squared Error (RMSE) 6 Lag 1 Autocorrelation of Error (ACF1) 7 Correlation between the Actual and the Forecast (corr) 8 Min-Max Error (minmax)