A Practical Approach to Timeseries Forecasting Using Python - Quiz - Data Processing for Timeseries Forecasting

The video tutorial covers the concepts of stationary and non-stationary time series data. It introduces four methods to convert non-stationary time series into stationary ones. The tutorial provides practical instructions to download a dataset and apply the Dickey Fuller test to check for stationarity. If the dataset is non-stationary, it suggests using the square root method to convert it into a stationary time series and discusses the results.

What is the primary difference between stationary and non-stationary time series data?

• Non-stationary data is always linear.

• Stationary data has constant mean and variance over time.

• Stationary data changes its mean and variance over time.

• Non-stationary data has constant mean and variance over time.

How many methods are generally used to convert non-stationary time series into stationary?

• Two

• Five

• Three

• Four

Which test is used to check the stationarity of a dataset?

• Augmented Dickey-Fuller test

• T-test

• Chi-square test

• ANOVA test

What method should be applied if a dataset is found to be non-stationary?

• Square root method

• Logarithmic transformation

• Differencing

• Exponential smoothing

What is the purpose of applying the square root method to a time series?

• To stabilize the variance

• To convert it into a non-stationary series

• To make the data linear

• To increase the variance