  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.
