Advanced-Time-series-analysis

Hybrid models are one of the most widely-used hybrid models that in which a time series is assumed to be composed of two linear and nonlinear components. In this project, the performance of two types of these hybrid models is evaluated for predicting stock prices in order to introduce the more reliable series hybrid model. For this purpose, ARIMA, LSTM and MLPs are elected for constructing hybrid models. Empirical results for forecasting data sets indicate that despite of more popularity of the conventional ARIMA-LSTM model, the ARIMA-MLP hybrid model can overall achieved more accurate results.