A Practical Approach to Timeseries Forecasting Using Python - Course Introduction

This video tutorial covers Python usage, data manipulation with pandas and numpy, and data visualization using matplotlib and seaborn. It introduces machine learning algorithms like ARIMA and SARIMA for time series forecasting and explores deep learning with RNNs, LSTM, and Bi-LSTM. The course concludes with practical projects on COVID-19 prediction, stock forecasting, and birth rate analysis.

Which library is primarily used for data manipulation in Python?

• scikit-learn

• pandas

• seaborn

• matplotlib

What type of plots can be used to visualize data relationships?

• Scatter plots

• Bar charts

• Line graphs

• Pie charts

Which library is used for data visualization alongside matplotlib?

• tensorflow

• numpy

• pandas

• seaborn

What is the purpose of using ARIMA in time series forecasting?

• To manipulate data

• To clean data

• To visualize data

• To forecast future data points

Why is data stationarity important in time series forecasting?

• It enhances data security

• It simplifies data manipulation

• It ensures consistent data patterns over time

• It helps in data visualization

Which type of neural network is best suited for sequential data?

• Generative Adversarial Network

• Feedforward Neural Network

• Recurrent Neural Network

• Convolutional Neural Network

What is the focus of the first project in the course?

• Stock predictions

• COVID-19 prediction

• Birth rate forecasting

• Weather forecasting