The Theta Method

A Simplified Approach to Time Series Decomposition

One method for predicting future values of a time series is the Theta method. It integrates two fundamental concepts: breaking down the time series and utilizing basic forecasting techniques to estimate future values.

This article sheds some light about its intuition and shows how to create an algorithm in Python based on the Theta method.

What is the Theta Method?

As every data science enthusiast knows, a time series is a sequence of data points collected or recorded at regular time intervals. For example, daily temperatures or the monthly values of an economic indicator.

Think of a time series as a combination of different components, trend (the general direction in which the data is moving), seasonality (regular patterns that repeat over time), and noise (random fluctuations that cannot be attributed to trend or seasonality).

The Theta method modifies the time series to highlight different components. This is done by adding or subtracting a trend component to/from the original series. For example, if we choose a Theta value of 2, we create a new series where the influence of the trend is doubled. If Theta is 0, it removes the trend entirely, focusing only on the cyclical and irregular…