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

The video tutorial covers the process of importing and plotting ACF and PACF using statsmodels. It then guides viewers through creating four different histograms, plotting autocorrelation, and calculating standard deviation and mean with rolling values. The tutorial concludes with displaying and interpreting the results, emphasizing changes in time series data.

What are the two functions imported from the statsmodels library for time series analysis?

• plot_ACF and plot_PACF

• plot_histogram and plot_scatter

• plot_trend and plot_seasonality

• plot_mean and plot_std

Which layout configuration is used for creating the four histograms?

• 3x3

• 2x2

• 1x4

• 4x1

What is the title given to the plot showing the original data distribution?

• Mean Over Time

• Autocorrelation

• Original Histogram

• Standard Deviation

What is the purpose of taking the first 365 values of the series?

• To represent a day's data

• To represent a month's data

• To represent a year's data

• To represent a week's data

Which axis is used for plotting the autocorrelation?

• mean_AX

• AC_AX

• hist_AX

• IST_STD_ax

What is the rolling value used for calculating the standard deviation?

• 10

• 30

• 15

• 7

What rolling value is used for calculating the mean over time?

• 60

• 30

• 15

• 7