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A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data

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dc.contributor.author Arslan, Serdar
dc.date.accessioned 2024-02-09T11:40:38Z
dc.date.available 2024-02-09T11:40:38Z
dc.date.issued 2022
dc.identifier.citation Arslan, S. (2022). "A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data", PeerJ Computer Science, Vol.8. tr_TR
dc.identifier.issn 23765992
dc.identifier.uri http://hdl.handle.net/20.500.12416/7139
dc.description.abstract For decades, time series forecasting had many applications in various industries such as weather, financial, healthcare, business, retail, and energy consumption forecasting. An accurate prediction in these applications is a very important and also difficult task because of high sampling rates leading to monthly, daily, or even hourly data. This high-frequency property of time series data results in complexity and seasonality. Moreover, the time series data can have irregular fluctuations caused by various factors. Thus, using a single model does not result in good accuracy results. In this study, we propose an efficient forecasting framework by hybridizing the recurrent neural network model with Facebook’s Prophet to improve the forecasting performance. Seasonal-trend decomposition based on the Loess (STL) algorithm is applied to the original time series and these decomposed components are used to train our recurrent neural network for reducing the impact of these irregular patterns on final predictions. Moreover, to preserve seasonality, the original time series data is modeled with Prophet, and the output of both sub-models are merged as final prediction values. In experiments, we compared our model with state-of-art methods for real-world energy consumption data of seven countries and the proposed hybrid method demonstrates competitive results to these state-of-art methods. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.7717/PEERJ-CS.1001 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Hybrid Model tr_TR
dc.subject Lstm tr_TR
dc.subject Prophet tr_TR
dc.subject Seasonality tr_TR
dc.subject Time Series Forecasting tr_TR
dc.title A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data tr_TR
dc.type article tr_TR
dc.relation.journal PeerJ Computer Science tr_TR
dc.contributor.authorID 325411 tr_TR
dc.identifier.volume 8 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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