Time Series Data

Working with Time Series Data

Time series data is a sequence of data points collected or recorded at successive points in time. Time series data is quite prevalent, especially in fields such as automotive, where sensor data is inherently time series; scientific research, in data such as ocean measurements and climate analysis; and finance, for stock prices and other indicators. Even if the time stamp isn’t essential to the analysis, the data often requires a time series approach, so it’s a significant part of what users engage with regularly.

Working with time series data can be tricky, but it’s a crucial skill for anyone dealing with data analysis. This guide provides answers to common questions and offers valuable resources to help you successfully tackle your next time series project.

2D plot of time series data over 65 years.

Time series data visualized in MATLAB. A time series is a collection of data points recorded sequentially over time. (See code.)

What Are the Key Characteristics of Time Series Data?

Three line plots of time series data, with a section of the plot highlighted to indicate classification importance.

To understand time series data classifications, you can create Grad-CAM and other visualizations in MATLAB. (See code.)

Time series data involves a temporal component—meaning time is involved. The key aspect is that it’s a series, with data points collected or recorded sequentially over time. This often implies regular intervals, such as every minute, second, or day, but regularity isn’t strictly required; having a time stamp is crucial. If your data set includes time stamps and requires sorting or analysis concerning time, you likely have time series data. It’s widely used in areas such as weather forecasting or financial analysis, where the order of data points over time is important for insights or predictions.

Time series data is characterized by having one of the columns as time, typically ordered. It represents measurements against specific time values, which can range from milliseconds to days. The data can have varying sample rates within the same series. Time series data isn’t just about specific data points at a given time but can also represent measurements over a period, such as daily or weekly summaries.

How Do I Analyze Time Series Data?

MATLAB® has many built-in apps and functions you can use to preprocess time series data. The choice of technique depends on your familiarity with the data and the cleaning process. If you’re new to the data set, starting with an interactive app such as the Data Cleaner app is a best first step because it offers an intuitive interface for initial exploration and cleaning. For specific purposes, such as synchronization and resampling, Live Editor tasks can be helpful. If you prefer coding and are familiar with the functions, you can directly use command-line functions.

Four visualizations of signals in the time and time-frequency domains.

Signal Analyzer app in MATLAB. (See documentation.)

Two key components of working with time series data are synchronization and resampling. Synchronization involves aligning data points based on their time stamps, ensuring data sets are comparable and analyses are accurate. Resampling is essential for adjusting the frequency of data points to meet specific analysis goals. MATLAB provides the synchronize and retime commands to help combine data sets at specific time points. If you’re unsure about using these functions, Live Editor tasks offer an interactive way to apply them and explore different options.

There are many techniques for time series analysis, but two key approaches are trend analysis and seasonality:

  • Trend analysis involves identifying and analyzing long-term movements in the data to understand the overall direction, which can help in making informed predictions or decisions.
  • Seasonality involves detecting and modeling regular patterns that repeat over specific periods, such as yearly, monthly, or daily cycles. These periods can be even more granular when it comes to sensors: Engineers in automotive industries need to think on the order of nano- or microsecond cycles, which can be crucial for accurate forecasting.

MATLAB offers tools and functions to perform these techniques efficiently, including built-in functions for data cleaning, visualization, and advanced modeling. The timetable table type is particularly useful for organizing and managing time series data, while the Signal Analyzer app provides a powerful platform for visualizing and analyzing signals interactively.

What Challenges Do Engineers Face Regarding Time Series Data?

Engineers often encounter challenges such as dealing with missing data due to sensor dropouts, handling outliers, and managing noise in the data. Time series data typically requires cleaning and preparation before it can be effectively used for modeling.

A major challenge is ensuring that time series data is properly sequenced and arranged. Even if the data doesn’t require extensive processing, it must be structured logically for analysis or machine learning models. Understanding seasonality and trends is crucial, as these patterns can sometimes be artifacts rather than genuine features. If not addressed, these artifacts can mislead models, especially in deep learning, where they might perpetuate false patterns. Additionally, engineers need a solid understanding of time series fundamentals, such as seasonality and stationarity, before diving into complex analyses. This foundational knowledge is sometimes lacking in those who primarily work in machine learning without an engineering background.

Another challenge specifically for MATLAB users is the confusion between “time series” as a concept and the MATLAB timeseries object. Users often search for “time series” but may not be aware of the timetable functionality, which offers more advanced features for handling time-based data. Using timetable in MATLAB enables you to create visualizations, prepare data, and perform other tasks. It supports synchronization and other advanced operations essential for effective time series data management.

Animation illustrating the synchronization of multirate data into one data set. The times are aligned and the data is resampled accordingly.

Going forward, MATLAB development will focus on the timetable functionality; the traditional timeseries object is now in maintenance mode. This approach will enable enhanced features and capabilities, better supporting MATLAB users working with time series data.

What’s Next? Where Do Engineers Go from Here?

Screenshot showing two line plots of time-stamped data and a series of buttons as next steps.

Preprocessing and organizing time series data using the Data Cleaner app in MATLAB. (See documentation.)

MATLAB offers several tools and functions for cleaning time series data. You can use command-line functions if you prefer coding or leverage low-code capabilities such as the Data Cleaner app or Live Editor tasks. These tools help you synchronize data, fill missing values using methods such as linear interpolation, and more.

The next steps depend on your application, but here are four paths to explore:

  • Examples: Check out examples related to signal processing and the frequency domain. These can introduce you to more sophisticated techniques that might be useful.
  • Signal Processing Onramp: Signal Processing Onramp serves as a great starting point for those new to these concepts and dealing with signal data.
  • Deep learning and machine learning examples: Look into examples specifically for time series forecasting within deep learning and machine learning contexts. These examples can highlight relevant techniques and applications.
  • Real-time applications: For those interested in real-time processing or model updates, consider exploring streaming applications. However, it’s important to understand foundational concepts before diving into real-time applications.

See also: time series analysis