A Practical Approach to Timeseries Forecasting Using Python - Feature Engineering

The video tutorial covers feature engineering in time series data, focusing on handling missing values, outliers, and coding categorical features. It demonstrates extracting date components like day, month, and year, and performing numerical transformations such as rounding decimals. The tutorial concludes with an introduction to stationarity in time series analysis.

What is one of the primary tasks of feature engineering in time series data?

• Developing machine learning models

• Designing user interfaces

• Handling missing values

• Creating new datasets

Which method is used to reset the index of a dataset before extracting date components?

• index_reset()

• reset_index()

• index_set()

• set_index()

What components are extracted from a date in the dataset?

• Century, decade, year

• Hour, minute, second

• Day, month, year

• Week, quarter, year

How can you ensure a column in a dataset has a maximum of three decimal places?

• Use the truncate() function with decimals=3

• Use the floor() function with decimals=3

• Use the ceil() function with decimals=3

• Use the round() function with decimals=3

Which library is used for rounding decimal values in the dataset?

• Matplotlib

• Pandas

• NumPy

• SciPy

What is one of the numerical transformations mentioned besides rounding?

• Data visualization

• Data multiplication

• Data compression

• Data encryption

What is the next topic introduced after numerical transformations?

• Machine learning models

• Stationarity in time series

• Data visualization

• Data cleaning