Henriques, Roberto André PereiraAkstinaite, Akvilina2023-02-072023-02-072023-01-23http://hdl.handle.net/10362/148773Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study aims to analyze the performance of the ensemble model – the combination of Long ShortTerm Memory Recurring Neural Network model with the ARIMA model. We developed these models separately to perform the best on their own in predicting prices 1, 7, and 14 observations ahead while taking into account the last 30, 60 and 90 observations and checking if the combination of them outperforms the standalone models. We evaluated the models based on RMSE and their ability to predict the turning points. Models were developed and tested on two different types of securities – index S&P 500 and cryptocurrency Bitcoin (BTC). The combined methods demonstrated strong performance on BTC data set and gave at least 90% turning point prediction accuracy when predicting the price for one observation ahead. For the S&P 500 data set, the performance of the stacked model was poor – it outperformed the standalone models only in one test out of eighteen – while predicting prices one observation ahead, looking back at the past 30 observations.engReoccurring Neural NetworksDeep LearningLong Short-Term MemoryFinancial Time SeriesARIMAmaster thesis203212436