Abstract :

This project focuses on techniques and tools for handling Time Series Datasets. Main aim of this project is to make Stock Market Prediction and Forecasting using Stacked LSTM.

Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning.

It is a variety of recurrent neural networks (RNNs) that are capable of learning long-term dependencies, especially in sequence prediction problems. LSTM has feedback connections, i.e., it is capable of processing the entire sequence of data, apart from single data points such as images.

Two approaches for solving this problem

i) Univariate Time Series Analysis : univariate time series model here means that there are only one dependent variable on the time series model

ii) Multivariate Time Series Analysis: it consist of more than one time-dependent variable and each variable depends not only on its past values but also has some dependency on other variables.

STEPS :

1. Importing Libraries and Loading data

2. Cleaning data and sorting data by date.

3. Applying univariate approch by selecting Closing Price as target variables.

4. Exploration and Analysis of Datasets.

5. Preprocessing by MinMax Scaler.

6. Preparing train and test datasets.

7. Constructing Stacked LSTM model.

8. Evaluation of Model.

9. Forecasting for future 30 days.

10. Acknowledgement and References.