A Practical Approach to Timeseries Forecasting Using Python - Resampling

The video tutorial explains the process of resampling a time series to improve accuracy and quantify uncertainty. It focuses on performing weekly resampling on air pollution data, calculating the weekly mean, and plotting the results. The tutorial highlights the benefits of resampling, such as better trends and results, and mentions the possibility of monthly and yearly resampling. It concludes with a brief introduction to the next topic, noise in automatic time series decomposition.

What is the primary goal of resampling in time series analysis?

• To improve accuracy and quantify uncertainty

• To increase the size of the dataset

• To eliminate outliers from the data

• To decrease the complexity of the dataset

Which data frequency is used for resampling in the given tutorial?

• Daily

• Yearly

• Monthly

• Weekly

What is the next step after resampling the data weekly?

• Calculate the weekly median

• Calculate the weekly mean

• Perform a yearly resampling

• Plot the daily data

What is the purpose of plotting the weekly mean?

• To check for data errors

• To compare with monthly data

• To visualize the trend over weeks

• To identify outliers

What other resampling frequencies are mentioned besides weekly?

• Hourly and bi-monthly

• Quarterly and bi-weekly

• Monthly and yearly

• Daily and hourly