Inspect Seasonality

This component calculates autocorrelation with Pearson Correlation for lagged copies of time series. Additionally, it produces an interactive view that displays the Autocorrelation Function (ACF) Plot, Partial Autocorrelation Function (PACF) Plot, and detects the first local maximum of correlation for sign of dominant seasonality.

If you encoutner errors please verify that
Preferances > KNIME > Python (labs) > Python environment configuration
is set to bundled

Input ports

1. Type: Table
   Input Table
   Data containing selected column for the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis.

Output ports

1. Type: Flow Variable
   Data Table of detected local maximum
   This table contains a list of detected local maximum, their corresponding correlation, and lag value.
2. Type: Table
   Flow Variable containing seasonality lag
   This flow variable contains the lag value where the dominant seasonality might occur.