A Study on Stock Forecasting Using Deep Learning and Statistical Models
Himanshu Gupta Aditya Jaiswal
Predicting a fast and accurate model for stock price forecasting is been a
challenging task and this is an active area of research where it is yet to be
found which is the best way to forecast the stock price. Machine learning, deep
learning and statistical analysis techniques are used here to get the accurate
result so the investors can see the future trend and maximize the return of
investment in stock trading. This paper will review many deep learning
algorithms for stock price forecasting. We use a record of s&p 500 index data
for training and testing. The survey motive is to check various deep learning
and statistical model techniques for stock price forecasting that are Moving
Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL
CNN which are deep learning models. It will discuss various models, including
the Auto regression integration moving average model, the Recurrent neural
network model, the long short-term model which is the type of RNN used for long
dependency for data, the convolutional neural network model, and the full
convolutional neural network model, in terms of error calculation or percentage
of accuracy that how much it is accurate which measures by the function like
Root mean square error, mean absolute error, mean squared error. The model can
be used to predict the stock price by checking the low MAE value as lower the
MAE value the difference between the predicting and the actual value will be
less and this model will predict the price more accurately than other models.