A Practical Approach to Timeseries Forecasting Using Python - Module Overview - Project 2: Microsoft Corporation Stock

This video tutorial covers a project on time series forecasting using Python, focusing on Microsoft stock predictions with Recurrent Neural Networks, specifically LSTMs. The project involves data analysis, stationarity checks, and implementing various LSTM models, including stacked and bidirectional versions. The tutorial outlines the project flow, from data import and visualization to model implementation and performance comparison, to determine the best forecasting methodology.

What is the primary focus of the project discussed in the course?

• Predicting weather patterns using LSTMs

• Forecasting Microsoft stock prices using Recurrent Neural Networks

• Analyzing social media trends with neural networks

• Developing a new programming language for data analysis

Which libraries are mentioned for performing data analysis in the project?

• TensorFlow and Keras

• Scikit-learn and Matplotlib

• Pandas and Numpy

• Seaborn and Plotly

What is the purpose of performing a stationarity check in time series forecasting?

• To ensure the data is normally distributed

• To determine the correlation between variables

• To verify that the time series can be used for forecasting

• To identify outliers in the dataset

Which models are implemented in the project for stock prediction?

• Simple LSTM and GRU

• Decision Trees and Random Forest

• Stacked LSTM and Bi-LSTM

• CNN and RNN

What is the final step in the project workflow?

• Deployment of the best model

• Model training and validation

• Performance comparison of different models

• Data cleaning and preprocessing