Rapid Design of Neural Networks for Time Series Prediction
IEEE Computational Science & Engineering
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Analysis of Nonstationary Time Series Using Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Effective and efficient similarity search in time series
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Scaling and time warping in time series querying
The VLDB Journal — The International Journal on Very Large Data Bases
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust outlier detection using commute time and eigenspace embedding
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Support Vector Machines (SVMs) are a leading tool in machine learning and have been used with considerable success for the task of time series forecasting. However, a key challenge when using SVMs for time series is the question of how to deeply integrate time elements into the learning process. To address this challenge, we investigated the distribution of errors in the forecasts delivered by standard SVMs. Once we identified the samples that produced the largest errors, we observed their correlation with distribution shifts that occur in the time series. This motivated us to propose a time-dependent loss function which allows the inclusion of the information about the distribution shifts in the series directly into the SVM learning process. We present experimental results which indicate that using a time-dependent loss function is highly promising, reducing the overall variance of the errors, as well as delivering more accurate predictions.