MegazordNet: combining statistical and machine learning standpoints for time series forecasting

• URL: http://arxiv.org/abs/2107.01017v1
• Date: Wed, 23 Jun 2021 15:06:54 GMT
• Title: MegazordNet: combining statistical and machine learning standpoints for time series forecasting
• Authors: Angelo Garangau Menezes and Saulo Martiello Mastelini
• Abstract summary: MegazordNet is a framework that explores statistical features within a financial series combined with a structured deep learning model for time series forecasting. We evaluate our approach predicting the closing price of stocks in the S&P 500 using different metrics, and we were able to beat single statistical and machine learning methods.
• Score: 0.4061135251278187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting financial time series is considered to be a difficult task due to the chaotic feature of the series. Statistical approaches have shown solid results in some specific problems such as predicting market direction and single-price of stocks; however, with the recent advances in deep learning and big data techniques, new promising options have arises to tackle financial time series forecasting. Moreover, recent literature has shown that employing a combination of statistics and machine learning may improve accuracy in the forecasts in comparison to single solutions. Taking into consideration the mentioned aspects, in this work, we proposed the MegazordNet, a framework that explores statistical features within a financial series combined with a structured deep learning model for time series forecasting. We evaluated our approach predicting the closing price of stocks in the S&P 500 using different metrics, and we were able to beat single statistical and machine learning methods.

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