The nature of statistical learning theory
The nature of statistical learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Using support vector machines for time series prediction
Advances in kernel methods
Dynamic fuzzy data analysis based on similarity between functions
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Incremental Learning from Noisy Data
Machine Learning
Efficient mining method for retrieving sequential patterns over online data streams
Journal of Information Science
A Hybrid Forecasting Methodology using Feature Selection and Support Vector Regression
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
Towards a machine learning approach based on incremental concept formation
Intelligent Data Analysis
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Sequential learning in neural networks: A review and a discussion of pseudorehearsal based methods
Intelligent Data Analysis
A fast grid search method in support vector regression forecasting time series
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Dynamic clustering with soft computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy data mining: a literature survey and classification framework
International Journal of Networking and Virtual Organisations
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Traditional methodologies for time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques such as neural networks and support vector regression, have been successfully used to develop forecasting models. A problem that has not yet received proper attention, however, is how to update such forecasting models when new data arrives, i.e. when a new event of the considered time series occurs. This paper presents a strategy to update support vector regression based forecasting models for time series with seasonal patterns. The basic idea of this updating strategy is to add the most recent data to the training set every time a predefined number of observations takes place. This way, information in new data is taken into account in model construction. The proposed strategy outperforms the respective static version in almost all time series studied in this work, considering three different error measures.