A novel active multi-source transfer learning algorithm for time series forecasting
Authors:
- 21 September 2020
- journal article
- research article
- Published by Springer Nature in Applied Intelligence
- Vol. 51 (3) , 1326-1350
Abstract
No abstract availableKeywords
Funding Information
- National Basic Research Program of China (973 Program) (2018YFC2001600, 2018YFC2001602)
- National Natural Science Foundation of China (61473150)
This publication has 41 references indexed in Scilit:
- Chaotic time series prediction with employment of ant colony optimizationExpert Systems with Applications, 2012
- Cooperative coevolution of Elman recurrent neural networks for chaotic time series predictionNeurocomputing, 2012
- A novel hybridization of artificial neural networks and ARIMA models for time series forecastingApplied Soft Computing, 2011
- Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programmingKnowledge-Based Systems, 2011
- Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networksNeurocomputing, 2010
- Linear and nonlinear attributes of ultrasonic time series recorded from experimentally loaded rock samples and total failure predictionInternational Journal of Rock Mechanics and Mining Sciences, 2007
- Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and predictionFuzzy Sets and Systems, 2006
- Time-series prediction using a local linear wavelet neural networkNeurocomputing, 2006
- Support vector machine with adaptive parameters in financial time series forecastingIEEE Transactions on Neural Networks, 2003
- Accurate On-line Support Vector RegressionNeural Computation, 2003