Abstract
Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.
Highlights
Among time series predictions, Stock market prediction is considered as one of the most challenging problems, due to its noisy and unstable features [1]
First Fig. 5. presents the plot depicting the performance of Wavelet transform and Kalman filter for S&P500 index market
“LSTM” refers to the model which used the raw data, “WLSTM” refers to the model which used the data that has been denoised by Wavelet transform and “KLSTM” refers to the model which used data that has been smoothed by Kalman filter
Summary
Stock market prediction is considered as one of the most challenging problems, due to its noisy and unstable features [1]. Since the early 70s, extensive efforts have been commenced to predict stock prices by using new mathematical methods, time series, and more advanced tools including artificial intelligence and many tests on price information and stock indexes in countries such as United States, United Kingdom, Canada, Germany, and Japan to show the presence or absence of a specific structure in stock price information [2]. To analyze this issue, the use of deep learning has attracted a great deal of attention in recent years and has been mentioned much in the communities related to artificial intelligence and machine learning. The fourth section is dedicated to explanations about the project implementation and in the last part result of the project are discussed and analyzed
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