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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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-06-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1001.pdf |
| _version_ | 1818469251329032192 |
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| author | Serdar Arslan |
| author_facet | Serdar Arslan |
| author_sort | Serdar Arslan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T21:21:32Z |
| format | Article |
| id | doaj.art-41e068ed99b34df1a6e312367f15d6cb |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-13T21:21:32Z |
| publishDate | 2022-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-41e068ed99b34df1a6e312367f15d6cb2022-12-22T02:29:28ZengPeerJ Inc.PeerJ Computer Science2376-59922022-06-018e100110.7717/peerj-cs.1001A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series dataSerdar ArslanFor 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.https://peerj.com/articles/cs-1001.pdfTime series forecastingLSTMProphetHybrid modelSeasonality |
| spellingShingle | Serdar Arslan A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data PeerJ Computer Science Time series forecasting LSTM Prophet Hybrid model Seasonality |
| 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 | Time series forecasting LSTM Prophet Hybrid model Seasonality |
| url | https://peerj.com/articles/cs-1001.pdf |
| work_keys_str_mv | AT serdararslan ahybridforecastingmodelusinglstmandprophetforenergyconsumptionwithdecompositionoftimeseriesdata AT serdararslan hybridforecastingmodelusinglstmandprophetforenergyconsumptionwithdecompositionoftimeseriesdata |