Types of Time Series Decomposition Techniques

Additive Decomposition:

• In additive decomposition, the time series is expressed as the sum of its components:
• It’s suitable when the magnitude of seasonality doesn’t vary with the magnitude of the time series.
  

Multiplicative Decomposition:

• In multiplicative decomposition, the time series is expressed as the product of its components:
• It’s suitable when the magnitude of seasonality scales with the magnitude of the time series.

Methods of Decomposition

Moving Averages:

• Moving averages involve calculating the average of a certain number of past data points.
• It helps smooth out fluctuations and highlight trends.

Seasonal Decomposition of Time Series

• The Seasonal and Trend decomposition using Loess (STL) is a popular method for decomposition, which uses a combination of local regression (Loess) to extract the trend and seasonality components.

Exponential Smoothing State Space Model

• This method involves using the ETS framework to estimate the trend and seasonal components in a time series.

Time series data consists of observations taken at consecutive points in time. These data can often be decomposed into multiple components to better understand the underlying patterns and trends. Time series decomposition is the process of separating a time series into its constituent components, such as trend, seasonality, and noise. In this article, we will explore various time series decomposition techniques, their types, and provide code samples for each.

Time series decomposition helps us break down a time series dataset into three main components:

1. Trend: The trend component represents the long-term movement in the data, representing the underlying pattern.
2. Seasonality: The seasonality component represents the repeating, short-term fluctuations caused by factors like seasons or cycles.
3. Residual (Noise): The residual component represents random variability that remains after removing the trend and seasonality.

By separating these components, we can gain insights into the behavior of the data and make better forecasts.