A Practical Approach to Timeseries Forecasting Using Python - Autocorrelation, Standard Deviation, and Mean

The video tutorial covers plotting techniques using statsmodels, focusing on setting up subplots for histogram, standard deviation, autocorrelation, and mean over time. It explains how to import necessary functions, configure subplot layouts, and plot data from a DataFrame. The tutorial concludes with a discussion on performing a stationarity check before applying machine learning techniques.

Which library is used for plotting in the video tutorial?

• matplotlib

• seaborn

• pandas

• statsmodels

What is the purpose of importing plot_ACF and plot_pacf?

• To create scatter plots

• To plot autocorrelation and partial autocorrelation

• To generate bar charts

• To visualize data distributions

What is the specified figure size in the tutorial?

• 10x5

• 12x7

• 8x6

• 15x10

How many plots are arranged in the 2x2 layout?

• 2

• 3

• 4

• 5

Where is the histogram plot positioned in the grid layout?

• 1, 1

• 0, 1

• 1, 0

• 0, 0

What is the rolling value used for standard deviation?

• 5

• 7

• 10

• 15

Which axis setting is used for plotting the mean over time?

• std_AX

• AC_AX

• hist_AX

• mean_AX

What is the maximum value of autocorrelation shown in the plot?

• 0.5

• 1

• 1.5

• 2

What is the next step after plotting to prepare for machine learning?

• Feature scaling

• Data augmentation

• Data normalization

• Stationarity check

Which test is mentioned for checking stationarity?

• Augmented Dickey-Fuller test

• Phillips-Perron test

• Kwiatkowski-Phillips-Schmidt-Shin test

• Dickey-Fuller test