Methodology for long-term prediction of time series
TLDR
A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward-backward selection is introduced and is used to optimize the three input selection criteria (k-NN, MI and NNE).About:
This article is published in Neurocomputing.The article was published on 2007-10-01 and is currently open access. It has received 368 citations till now. The article focuses on the topics: Selection (genetic algorithm) & Long-term prediction.read more
Citations
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Journal ArticleDOI
Time-series data mining
Philippe Esling,Carlos Agon +1 more
TL;DR: A survey of the techniques applied for time-series data mining, namely representation techniques, distance measures, and indexing methods, is provided.
Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.
Journal ArticleDOI
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
TL;DR: Three findings appear to be consistently supported by the experimental results: Multiple-Output strategies are the best performing approaches, deseasonalization leads to uniformly improved forecast accuracy, and input selection is more effective when performed in conjunction with dese Masonalization.
Journal ArticleDOI
A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm
TL;DR: A hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasts model.
References
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