Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Learning to Recognize Time Series: Combining ARMA models with memory-based learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A novel nonlinear neural network ensemble model for financial time series forecasting
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Opinion Dynamics of Elections in Twitter
LA-WEB '12 Proceedings of the 2012 Eighth Latin American Web Congress
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Usually time series are controlled by generative processes which display changes over time. On many occasions, two or more generative processes may switch forcing the abrupt replacement of a fitted time series model by another one. We claim that the incorporation of past data can be useful in the presence of concept shift. We believe that history tends to repeat itself and from time to time, it is desirable to discard recent data reusing old past data to perform model fitting and forecasting. We address this challenge by introducing an ensemble method that deals with long-memory time series. Our method starts by segmenting historical time series data to identify data segments which present model consistency. Then, we project the time series by using data segments which are close to current data. By using a dynamic time warping alignment function, we try to anticipate concept shifts, looking for similarities between current data and the prequel of a past shift. We evaluate our proposal on non-stationary and non-linear time series. To achieve this we perform forecasting accuracy testing against well known state-of-the-art methods such as neural networks and threshold auto regressive models. Our results show that the proposed method anticipates many concept shifts.