Abstract
The field of time series forecasting has grown significantly over the past several years and is now highly active. In numerous application domains, deep neural networks are exact and powerful. They are among the most popular machine learning techniques for resolving big data issues because of these factors. Historically, there have been numerous methods for accurately predicting the subsequent change in time series data. The time series forecasting problem and its mathematical underpinnings are first articulated in this study. Following that, a description of the most popular deep learning architectures used to date with success in time series forecasting is provided, emphasizing both their benefits and drawbacks. Feedforward networks, recurrent neural networks (such as Elman networks), long- and short-term memory (LSTM), and gated recurrent units (GRU) are given special consideration. Furthermore, the advantages of the XGBoost boosting tree method have shown its superiority in numerous data mining competitions in recent years. The high coefficients of the metric measures indicate that the proposed XGBoost model provides good predictive performance, according to the results.
Published Version
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