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- W4318186082 abstract "Time series forecasting has historically been a popular research area, attracting widespread interest in academia and industry. Recently, Deep Learning based models like RNN, LSTM, and NeuralProphet have been successfully applied for time series forecasting. However, single forecasting models are unable to capture full information and learn complex patterns in data. The combination of models has proved to be an effective strategy to address this issue. Traditional hybrid structures still limit the forecasting ability of hybrid methods. In this paper, we propose a novel hybrid forecasting model based on LSTM and NeuralProphet (NP-LSTM), which is constructed by a parallel-series hybrid structure. The proposed model uses LSTM to model diverse nonlinear relationships and NeuralProphet is designed to extract primary trends and seasonal effects and provide interpretability. In the experiments of this paper, we compare our model with other single models and hybrid models of traditional structures using four real-world datasets. The experimental results show that our NP-LSTM hybrid model obtains superior performance in various metrics for time series forecasting." @default.
- W4318186082 created "2023-01-27" @default.
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- W4318186082 date "2022-12-17" @default.
- W4318186082 modified "2023-09-27" @default.
- W4318186082 title "A Hybrid Model Based on NeuralProphet and Long Short-Term Memory for Time Series Forecasting" @default.
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- W4318186082 doi "https://doi.org/10.1109/bigdata55660.2022.10020471" @default.
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