Multi-Predictor Forecasting Task
A Multi-Predictor Forecasting Task is a forecasting task that is a multi-predictor prediction task.
- Context:
- Input: a Multi-Predictor Learning Dataset.
- It can be solved by a Multi-Predictor Forecasting System (that implements a Multi-Predictor Forecasting algorithm).
- It can range from being a Fully-Supervised Multi-Predictor Forecasting Task to being a Semi-Supervised Multi-Predictor Forecasting Task.
- It can range from being a Multivariate Forecasting Task to being a Mixed Multi-Predictor Forecasting Task (such as conditional forecasting).
- Example(s):
- Counter-Example(s):
- See: Multi-Predictor Prediction Task, Curse of Dimensionality.
References
2007
- (Heij et al., 2007) ⇒ Christiaan Heij, Patrick J. F. Groenen, and Dick van Dijk. (2007). “Forecast Comparison of Principal Component Regression and Principal Covariate Regression.” In: Computational Statistics & Data Analysis Journal, 51(7). doi:10.1016/j.csda.2006.10.019
- QUOTE: Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. The forecast accuracy of two methods for dealing with many predictors is compared, that is, principal component regression (PCR) and principal covariate regression (PCovR).
2006
- (Stock & Watson, 2006) ⇒ James H. Stock, and Mark W. Watson. (2006). “Chapter 10 Forecasting with Many Predictors.” In: Handbook of Economic Forecasting, 1. doi:10.1016/S1574-0706(05)01010-4
- Academic work on macroeconomic modeling and economic forecasting historically has focused on models with only a handful of variables. In contrast, economists in business and government, whose job is to track the swings of the economy and to make forecasts that inform decision-makers in real time, have long examined a large number of variables. In the U.S., for example, literally thousands of potentially relevant time series are available on a monthly or quarterly basis. The fact that practitioners use many series when making their forecasts – despite the lack of academic guidance about how to proceed – suggests that these series have information content beyond that contained in the major macroeconomic aggregates. But if so, what are the best ways to extract this information and to use it for real-time forecasting?
This chapter surveys theoretical and empirical research on methods for forecasting economic time series variables using many predictors, where “many” can number from scores to hundreds or, perhaps, even more than one thousand. Improvements in computing and electronic data availability over the past ten years have finally made it practical to conduct research in this area, and the result has been the rapid development of a substantial body of theory and applications. This work already has had practical impact – economic indexes and forecasts based on many-predictor methods currently are being produced in real time both in the US and in Europe – and research on promising new methods and applications continues.
- Academic work on macroeconomic modeling and economic forecasting historically has focused on models with only a handful of variables. In contrast, economists in business and government, whose job is to track the swings of the economy and to make forecasts that inform decision-makers in real time, have long examined a large number of variables. In the U.S., for example, literally thousands of potentially relevant time series are available on a monthly or quarterly basis. The fact that practitioners use many series when making their forecasts – despite the lack of academic guidance about how to proceed – suggests that these series have information content beyond that contained in the major macroeconomic aggregates. But if so, what are the best ways to extract this information and to use it for real-time forecasting?