Abstract:
The motive behind researching on “TIME SERIES ANALYSIS OF STOCK PRICE
PREDICTION USING HYBRID DEEP LEARNING NEURAL NETWORK” is to
explore the thoroughness of the historical financial data of a stock suffice to make
cabbalistic foreboding about its aspect prices with the use of Machine Learning. The
purpose of my task is that, as the price of a stock fluctuates with time dimension, it is
occupied to go through with specific patterns which I prospect to capture using Deep
Learning and utilize for future predictions. At first, I will amplify on the necessary of
theoretical background information regarding Machine Learning , concentrating on
particularly the neural networks that will later be used. Pursuing that, I will experiment
that how existing research about stock market forecasting using corresponding techniques
prosecuted in the past and I will propose a model in this research using Hybrid Long
short-term memory (LSTM), a Recurrent Neural Network architecture which is the most
suitable method for this kind of analytical tasks. I have worked with around 1132 data
which were collected from online platform. Here, I have analyzed error fluctuation of the
collected data through one proposed model rather than analyzing the result of different
model accuracy. At last, I will try to make predictions about the future trajectories of the
stocks’ prices and draw consequences from them.
