What Is Time Series Decomposition? A Detailed Introduction
Charles Mahler /
Aug 11, 2023
Businesses increasingly use time series data to understand changing trends and patterns. By analyzing temporal data patterns, companies can better forecast demand, allocate resources, predict customer behavior, and make informed financial decisions.
But in order to extract value from time series datasets, analysts must break down measurements and observations into smaller components. The easiest way to accomplish this is with time series decomposition.
Understanding time series decomposition is critical for any business that wants to unlock the value of time series forecasting. In this post, you’ll learn what time series decomposition is, why it’s necessary, and some common techniques.
What Is Time Series Decomposition?
Time series decomposition is a statistical process for breaking down a time series dataset into individual components.
Software engineers and data analysts use time series decomposition to discover patterns and variations within time series datasets. This process simplifies complex data, making it easier to model and forecast future data points, identify anomalies, and make accurate data-driven decisions.
What Is an Example of Time Series Decomposition?
Time series decomposition is common in various fields, including economics, retail, healthcare, manufacturing, and logistics. It’s applicable to any organization looking to analyze historical data and predict future outcomes.
One common example of time series decomposition involves studying in-store customer traffic over several years. To accomplish this, a business would start with raw time series data and apply various decomposition techniques. Through decomposition, the business could examine factors like the overall growth or decline in store traffic, recurring seasonal patterns, and variations that may require further analysis.
Time Series Decomposition Formulas
Decomposition involves breaking down time series data into three components: trend, seasonality, and noise.
Trend refers to the pattern or movement of a dataset over time. In other words, it indicates the overall direction that data is moving. A trend can increase, decrease, or remain flat over time depending on how the data changes.
Seasonality describes recurring patterns that take place within specific time intervals. For example, patterns may happen daily, weekly, monthly, or bi-annually.
Oftentimes, datasets may contain random variations—or noise—that do not relate to the trend or seasonality. This includes irregular events and other factors that contribute to short-term fluctuations.
Time Series Decomposition Techniques
In order to extract value from a time series, you first need to manipulate it by separating the data into constituent components. With this in mind, two common methods for decomposition are the additive model and the multiplicative model.
The additive model represents the time series as the sum of its individual components: trend, seasonal, and noise (residual). By adding these components together, you can reconstruct the original time series data.
This model is necessary for time series data where the seasonal fluctuations are consistent in their amplitude across the entire time series. It’s a good option when seasonal patterns indicate a constant degree of variation compared to the overall data level.
For example, the additive model would be appropriate for a travel agency analyzing monthly bookings for a particular destination where the seasonal changes have stable absolute values.
The multiplicative model represents a time series as a product of its components. In other words, it involves multiplying components to determine data behavior.
You can use the multiplicative model to understand data patterns and behavior when there are measurements from different points in time. To illustrate, a car insurance company could use the multiplicative model to determine seasonal variations in traffic violations or claims. A dataset might reveal that claims go up or down in the winter due to seasonal fluctuations. This could also reveal certain trends—like certain years having more claims than others.
How Time Series Decomposition Works in Various Languages
When it comes to time series decomposition, data analysts can choose from a wide variety of programming languages. As a result, analysts and data scientists can leverage their preferred tools and services—making the process flexible and convenient.
With that in mind, here’s a breakdown of how decomposition works across some common programming languages:
Many data analysts prefer to use Python for time series decomposition because it offers a vast ecosystem of libraries and packages. In addition, Python has a user-friendly syntax which makes it accessible to individuals with varying levels of programming expertise. Python also offers a wide range of visualization tools, making it easier to understand and share discoveries.
R is another popular language for time series decomposition. It offers a large ecosystem of time series packages and a range of useful functions and tools. It also offers numerous time series decomposition methods, including additive and multiplicative models.
Like Python, R also offers strong data visualization packages. Plus, it’s suitable for statistical analysis, meaning analysts can apply advanced techniques like hypothesis tests and residual analysis.
Julia is becoming an increasingly popular language for time series decomposition, especially when working with large data sets. This language offers exceptional performance, as it combines the speed of low-level languages like Fortran and C with a high-level, user-friendly syntax.
It’s also easy to use and seamlessly interoperates with other languages like Python, R, and C/C++. In addition, Julia offers extensive packages and libraries for data analysis, along with built-in support for distributed and parallel computing.
Rust is another language that is known for its performance, providing high-speed execution for large time series datasets. What’s more, the language provides predictable performance, which is important for time series decomposition where variations can lead to unreliable results. Analysts can also create custom decomposition algorithms with Rust, providing full control over algorithm implementation, memory management, and low-level optimizations.
Frequently Asked Questions
Can You Perform Time Series Decomposition in Excel?
It is possible to use Excel’s data analysis tools and functions to decompose a time series into constituent components. For example, you can use Excel to calculate moving averages, subtract calculated trend values from original observations, estimate and remove seasonality, and calculate residuals. In addition, Excel makes it easy to create charts and plots for visualization purposes.
What’s the Difference Between Time Series Decomposition and Regression?
Time series decomposition and regression are two techniques for modeling and exploring data, each with a distinct purpose:
- Time series decomposition is typically used for analyzing time series data from different periods and at regular intervals. The primary objective is to break down a time series into smaller components.
- Regression is a method for modeling relationships between variables. It applies to any type of data, regardless of time. It’s useful for exploring the association between one or more predictors and outcomes.
What Is the Classical Time Series Decomposition Technique?
The classical time series decomposition technique is a method for rearranging a time series into trend, seasonality, and noise components.
This method involves representing the time series as the sum of those three components. It’s often a starting point for time series analysis because it offers a structured approach to identifying data patterns.
Supercharge Time Series Data Analysis With InfluxDB
Speed and efficiency are critical for success with time series decomposition. Engineers and analysts must be able to quickly forecast and detect anomalies in real time.
However, not all databases offer the same level of performance and usability, which can make time series data analysis challenging.
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