Original Contribution: Stacked generalization
Neural Networks
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Local Modeling Using Self-Organizing Maps and Single Layer Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Vector quantization using information theoretic concepts
Natural Computing: an international journal
Trainable fusion rules. I. Large sample size case
Neural Networks
Adaptive mixtures of local experts
Neural Computation
| Hi-index | 12.05 |
Many current technological challenges require the capacity of forecasting future measurements of a phenomenon. This, in most cases, leads directly to solve a time series prediction problem. Statistical models are the classical approaches for tackling this problem. More recently, neural approaches such as Backpropagation, Radial Basis Functions and recurrent networks have been proposed as an alternative. Most neural-based predictors have chosen a global modelling approach, which tries to approximate a goal function adjusting a unique model. This philosophy of design could present problems when data is extracted from a phenomenon that continuously changes its operational regime or represents distinct operational regimes in a unbalanced manner. In this paper, two alternative neural-based local modelling approaches are proposed. Both follow the divide and conquer principle, splitting the original prediction problem into several subproblems, adjusting a local model for each one. In order to check their adequacy, these methods are compared with other global and local modelling classical approaches using three benchmark time series and different sizes (medium and high) of training data sets. As it is shown, both models demonstrate to be useful pragmatic paradigms to improve forecasting accuracy, with the advantages of a relatively low computational time and scalability to data set size.