Time Series Classical Decomposition

This component aims to decompose a Time Series using the Classical Decomposition approach. The component has both a visual and data output that produces the following elements:
- Detrended Time Series
- Deseasonalized Time Series
- Seasonal Factors
- Fitted Time Series
- Error component

Either the method with Additive Seasonality or the method with Multiplicative Seasonality can be selected. Moreover you can select between two trend estimation methods (Centered Moving Average and Polynomial Curve FItting)

Options

Time Series Column Selection (PRE-SORTED by time): Time Series Column Selection (before using this component, please SORT your Time Series in order to keep the temporal sequence)
Length of the Seasonal cycle: Insert the length of the Seasonal pattern (e.g. 12 for monthly data, 4 for quarterly data, etc.)
Degrees of Polynomial Curve Fitting: Trend - Polynomial Curve Fitting Degrees
Trend Estimation Method: Select the trend estimation method
Decomposition Method: Select the decomposition method

Input Ports

: Insert Time Series Data (please SORT your Time Series in advance in order to keep the temporal sequence of the data)

Output Ports

: Decomposition columns (Estimated Trend, Detrended Series, Seasonal Factors, Deseasonalized Series, Errors, Fitted Series) plus original columns

Time Series Classical Decomposition

Options

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Output Ports

Nodes

Extensions

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