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

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 leadi...

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Autor principal: Arslan, Serdar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202617/
https://www.ncbi.nlm.nih.gov/pubmed/35721410
http://dx.doi.org/10.7717/peerj-cs.1001
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author Arslan, Serdar
author_facet Arslan, Serdar
author_sort Arslan, Serdar
collection PubMed
description 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.
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spelling pubmed-92026172022-06-17 A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data Arslan, Serdar PeerJ Comput Sci Data Mining and Machine Learning 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. PeerJ Inc. 2022-06-10 /pmc/articles/PMC9202617/ /pubmed/35721410 http://dx.doi.org/10.7717/peerj-cs.1001 Text en © 2022 Arslan https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Arslan, Serdar
A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title_full A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title_fullStr A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title_full_unstemmed A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title_short A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
title_sort hybrid forecasting model using lstm and prophet for energy consumption with decomposition of time series data
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202617/
https://www.ncbi.nlm.nih.gov/pubmed/35721410
http://dx.doi.org/10.7717/peerj-cs.1001
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