A Practical Approach to Timeseries Forecasting Using Python - Projects to Be Covered

This course covers three projects: COVID-19 predictions using machine learning, stock prediction with RNNs, and birth rate forecasting using deep learning. Each project involves data preprocessing, visualization, and model implementation, with a focus on making data stationary and comparing model performances.

What is the primary goal of making data stationary in the COVID-19 predictions project?

• To reduce data size

• To improve data visualization

• To enhance model accuracy

• To simplify data preprocessing

Which two models are implemented in the COVID-19 predictions project?

• ARIMA and SARIMA

• SVM and Random Forest

• RNN and CNN

• LSTM and bi-LSTM

In the stock prediction project, which method is used to check for stationarity?

• Log method

• At Fuller method

• Square method

• Differencing method

What type of models are used in the birth rate forecasting project?

• Hybrid models

• Statistical models

• Deep learning models

• Machine learning models

Which of the following is NOT a model implemented in the birth rate forecasting project?

• LSTM

• Stacked LSTM

• ARIMA

• Bi-LSTM