A Novel Hybrid Approach for Stock Market Index Forecasting using CNN-LSTM Fusion Model
Keywords:
Long Short-Term Memory Networks, Multiple Time Scale Features, Stock Forecasting, Share market index, Machine Learning, Neural networksAbstract
Forecasting the path of currency exchange rates in the stock market is a highly investigated and demanding endeavour for investors and researchers in the current dynamic environment. The inherent unpredictability of the market makes it extremely challenging to forecast financial markets with a fair level of confidence. The emergence of artificial intelligence has witnessed intricate algorithms yielding encouraging results in the prediction of stock market trends. Predicting stock prices is essential for formulating a trading strategy and identifying favourable times to purchase or sell equities. Financial time series display heterogeneous time scale characteristics as a result of differing durations of influential variables and distinct trading behaviours of market participants.In order to tackle the difficulty of predicting stock values, we suggest utilising a model called the features Integration LSTM-CNN paradigm. This model combines features acquired from diverse representations of same data, especially, stock time series and stock chart graphics. The fusion neural network, in collaboration with the raw daily price series and the initial and secondary layers of the CNN, extracts diverse characteristics over several interval scales. These qualities encompass the price sequence's short-term, medium-term, and long-term attributes. The hybrid neural network presented incorporates LSTM deep convolution networks to effectively include temporal dependencies across the three categories of information. Subsequently, fully connected layers are utilised to generate complete visual representations for predicting the price trajectory.
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