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• W4200537258 abstract "Over the past decades, the hybrid models which integrate parametric and non-parametric learning models have proven to be a viable method in time series forecasting. Several structures were proposed as a combination of parametric models such as autoregressive moving average (ARIMA) and non-parametric models such as artificial neural network (ANN). Although these models show superior performance than a single model, there is the scope of further improvement if the underlying feature laid in the original data is augmented before applying models. In this work, empirical wavelet transform (EWT) is implemented to decompose the original series into several sub-series which contain unique features at different frequency horizon. The sub-series are then used along with moving average filter (MA), ARIMA and ANN to perform forecasting tasks. The experiments were performed on four public real-world data sets and compared to the other six benchmark models. The results showed that the proposed model achieved considerably higher forecast accuracy." @default.
• W4200537258 created "2021-12-31" @default.
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• W4200537258 date "2021-11-01" @default.
• W4200537258 modified "2023-10-16" @default.
• W4200537258 title "Enhancing Neural Network Based Hybrid Learning with Empirical Wavelet Transform for Time Series Forecasting" @default.
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• W4200537258 doi "https://doi.org/10.1109/ictai52525.2021.00063" @default.
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