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

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

SUBMITTER: Arslan S 

PROVIDER: S-EPMC9202617 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature