Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network
  Model
Aadhitya A Rajapriya R Vineetha R S Anurag M Bagde
Stock market is often important as it represents the ownership claims on
businesses. Without sufficient stocks, a company cannot perform well in
finance. Predicting a stock market performance of a company is nearly hard
because every time the prices of a company stock keeps changing and not
constant. So, its complex to determine the stock data. But if the previous
performance of a company in stock market is known, then we can track the data
and provide predictions to stockholders in order to wisely take decisions on
handling the stocks to a company. To handle this, many machine learning models
have been invented but they didn't succeed due to many reasons like absence of
advanced libraries, inaccuracy of model when made to train with real time data
and much more. So, to track the patterns and the features of data, a CNN-LSTM
Neural Network can be made. Recently, CNN is now used in Natural Language
Processing (NLP) based applications, so by identifying the features from stock
data and converting them into tensors, we can obtain the features and then send
it to LSTM neural network to find the patterns and thereby predicting the stock
market for given period of time. The accuracy of the CNN-LSTM NN model is found
to be high even when allowed to train on real-time stock market data. This
paper describes about the features of the custom CNN-LSTM model, experiments we
made with the model (like training with stock market datasets, performance
comparison with other models) and the end product we obtained at final stage.