Prepared for the Journal of Economic Perspectives. The authors would like to thank David Pennock, Emile Servan-Schreiber of NewsFutures, David Dempsey and John Delaney of Tradesports, Alison Fealey and Oliver Frankel of Goldman Sachs, and George Neumann of IEM for help with data. Thanks to Kay-Yut Chen, Robin Hanson, Jim Hines, Andrew Leigh, Betsey Stevenson, Timothy Taylor, Hal Varian, Michael...

There are many systems that can be described as chaotic: The readings from seismic monitoring stations in mines which describe the rock dynamics, from EKG which describe the fibrillation of a cardiac patient’s heart, and the share prices in financial markets which describe the optimism about the earning potential of companies are examples of observations of deterministic, non−linear, dynamical ...

We describe the use of ensemble methods to build proper models time series prediction. Our approach extends the classical ensemble methods for neural networks by using several different model architectures. We further suggest an iterated prediction procedure to select the final ensemble members. This is an extension of well know the crossvalidation scheme for model validation.

This paper demonstrates how the selection of Prediction Strategy is important in the Long-Term Prediction of Time Series. Two strategies are already used in the prediction purposes called Recursive and Direct. This paper presents a third one, DirRec, which combines the advantages of the two already used ones. A simple k -NN approximation method is used and all three strategies are applied to tw...

In this paper we propose a new method for volatile time series forecasting using Independent Component Analysis (ICA) algorithms and SavitzkyGolay filtering as preprocessing tools. The preprocessed data will be introduce in a based radial basis functions (RBF) Artificial Neural Network (ANN) and the prediction result will be compared with the one we get without these preprocessing tools or the ...

We consider the problem of time series prediction in the presence of missing data. We cast the problem as an online learning problem in which the goal of the learner is to minimize prediction error. We then devise an efficient algorithm for the problem, which is based on autoregressive model, and does not assume any structure on the missing data nor on the mechanism that generates the time seri...

Neural Network approaches to time series prediction are briefly discussed, and the need to find the appropriate sample rate and an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the appropriate sampling rate and embedding dimension, and thence window size, are discussed. The method is appli...

Time series forecasting is a challenge in many fields. In finance, one forecasts stock exchange courses or stock market indices; data processing specialists forecast the flow of information on their networks; producers of electricity forecast the load of the following day. The common point to their problems is the following: how can one analyze and use the past to predict the future? Many techn...

In this paper, variable selection and variable scaling are used in order to select the best regressor for the problem of time series prediction. Direct prediction methodology is used instead of the classic recursive methodology. Least Squares Support Vector Machines (LS-SVM) are used in order to avoid local minimal in the training phase of the model. The global methodology is applied to the tim...

As a research focus of intelligence algorithm, the prediction of classic noiseless chaotic time series has a great development in recent years. However, the existing prediction models cannot get good performance for real-world chaotic time series because of the interference of noise components. In order to take full advantage of the property of real-world chaotic time series, the paper proposes...