Predictive data mining: a practical guide
Predictive data mining: a practical guide
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
A radial basis function approach to financial time series analysis
A radial basis function approach to financial time series analysis
Time series data mining: identifying temporal patterns for characterization and prediction of time series events
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
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Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data.