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 feature engineering and how does it relate to time series data?

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What is the significance of handling missing values and outliers in feature engineering?

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Describe the process of extracting day, month, and year from a date in a dataset.

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Explain the importance of resetting the index in a dataset.

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

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What are some numerical transformations that can be performed on a dataset?

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Discuss the concept of stationarity in time series analysis.

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