Forecasting · Decomposition · Telecom · Tier A

Turning Decomposition into a Forecasting Strategy

The best decomposition is the one that creates the most predictable sub-signals. Decomposition is not preprocessing — it is a modeling decision layer.

ForecastingTime SeriesRTelecom · Fibre

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TL;DR: Decomposition is no longer just a preprocessing step. It becomes a modeling decision layer: the best decomposition is the one that creates the most predictable sub-signals.

The Problem

A raw weekly time series often mixes several dynamics at once: trend, seasonality, holiday effects, calendar distortions, and irregular noise.

Fitting one single model directly on the raw signal can work, but it forces the model to explain very different behaviors at the same time.

The Approach

Generate multiple decompositions of the series
Split each decomposition into a seasonally adjusted component and a seasonal component
Select the decomposition with the lowest combined residual error
Forecast each component with the model family best suited to its behavior
Recombine everything into the final forecast

Implementation

Adjusted component

Modeled with ARIMA + calendar/holiday regressors

Seasonal component

Modeled separately with ETS-based specifications

Combination

Decomposition forecast combined with Prophet and NNETAR

Stabilization

Final output stabilized with rolling median smoothing across iterations

Key Takeaway

Design insight: Decomposition is no longer just a preprocessing step. It becomes a modeling decision layer: the best decomposition is the one that creates the most predictable sub-signals.

Aggregate → Forecast → Disaggregate →Forecasting Routing Layer →Conformal Prediction →Nixtla Forecasting Libraries →MLforecast Forecasting Pipeline →Zero-Shot Forecasting Changes the Baseline →Foundation Models Raise the Baseline →

← Previousisnull() is a feature Next →Formula as Feature

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FAQ

What is the key takeaway from "Turning Decomposition into a Forecasting Strategy"?

Decomposition is no longer just a preprocessing step. It becomes a modeling decision layer: the best decomposition is the one that creates the most predictable sub-signals.

Who wrote this and what is it about?

This was written by Mahmoud Trigui, Senior Data Scientist. A forecasting architecture that uses decomposition as a modeling decision layer. Multiple decompositions, component-specific models, and ensemble recombination.