Predictive learning models for concept drift
Theoretical Computer Science - Algorithmic learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Enhancing DWT for recent-biased dimension reduction of time series data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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In recent years, dynamic time series analysis with the concept drift has become an important and challenging task for a wide range of applications including stock price forecasting, target sales, etc. In this paper, a recentness biased learning method is proposed for dynamic time series analysis by introducing a drift factor. First of all, the recentness biased learning method is derived by minimizing the forecasting risk based on a priori probabilistic model where the latest sample is weighted most. Secondly, the recentness biased learning method is implemented with an autoregressive process and the multi-layer feed-forward neural networks. The experimental results have been discussed and analyzed in detail for two typical databases. It is concluded that the proposed model has a high accuracy in time series forecasting.