Decompose Signal

Decomposes selected Time-Series or IoT signal into Trend, 2 Seasonal Components, and the remaining Residual.

Signal = T + S1 + S2 + R

[T] Trend Component: is calculated by fitting a regression model through the data with degree 2.
[S1] Seasonal Component 1: is calculated as the first major spike in auto-correlation.
[S2] Seasonal Component 2: is calculated as the first major spike in auto-correlation after the diferencing of the first seasonality.
[R] Residual: is what remains after trend and the two Seasonalities have been differenced.

The interactive displays shows the first 1000 records fpr the above outputs as well as the ACF plot for the detrended signal and both subsequent series after seasonality one and two are removed.

Required extensions:
KNIME Python Integration
(https://hub.knime.com/knime/extensions/org.knime.features.python2/latest)
KNIME Quick Forms
(https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest)
KNIME Math Expression (JEP)
(https://hub.knime.com/knime/extensions/org.knime.features.ext.jep/latest)
KNIME JavaScript Views
(https://hub.knime.com/knime/extensions/org.knime.features.js.views/latest)

Input ports

1. Type: Table
   Signal Data
   Table containing signal column

Output ports

1. Type: Table
   Table with Decomposed Signal
   Original table with Trend, Seasonality 1, Seasonality 2, and Residual columns added.
2. Type: PMML
   Trend Model
   The Regression model representing the Singal's Trend