This is the last post of the series on forecasting. Up to now, subjects such as varying factors, methods and evaluation criteria have been studied. This post lists key references in the field of time series prediction, particularly using Support Vector Regression (SVR). Note that some of these resources are not free:
- Time Series and Forecasting (chapter 22): different forecasting models are described in details in this book chapter. Evaluation criteria are also presented. Although several equations are given, business problems such as planning are mentioned.
- Support Vector Regression for link load prediction: SVR are detailed as well as specific studies on sensitivity to input data, SVR parameters and training size. Other topics such as complexity and forecast horizon are analyzed.
- Forecasting with computational intelligence – an evaluation of Support Vector Regression and Artificial Neural Networks for time series predictions: a technical paper detailing what are times series and how SVR and NN (Neural Networks) can be used for forecasting. Use of training, validation and test set is advised.
- Error measures for generalizing about forecasting methods – empirical comparisons: analysis of the different evaluation criteria. Clearly separate two common goals that are to i) select the best forecasting model and to ii) calibrate a given forecasting model.
- Support Vector Machines and learning about time: good introductory article with several time series principles explained. Different ways to approach forecasting are presented.
- Forecasting – principles and practice: this online textbook is my favorite resource to learn forecasting. Focus is made on concepts, key ideas and business challenges regarding time series prediction. If you should read only one resource about principles of forecasting, it should be this one.
- Another look at forecast – accuracy metrics for intermittent demand: concise article full of advices to choose the right evaluation criteria for forecasting. If you want to read only one reference about evaluation criteria, I strongly advise this one.
Feel free to comment and add you own references!