What Is Time Series Analysis?

Key takeaways

• Time series analysis is the study of data points over a set period of time at fixed intervals.

• Time series analysis techniques include the Box-Jenkins multivariate model, Box-Jenkins ARIMA model, and Holt-Winters method.

• The need for time series analysis arises from its ability to reveal trends in past behavior, supporting data-driven decision-making and accurate predictions.

To perform a time series analysis, identify the data points you want to examine and explore attributes such as seasonality, irregularity, cyclicity, and trends, using time series analysis techniques. Learn more about time series analysis and the benefits it can present. Then, consider earning the Google Data Analytics Professional Certificate, where you will have the opportunity to learn in-demand skills such as data visualization, data structures, data cleansing, spreadsheet software, and more.

What is time series analysis?

Time series analysis involves recording data points at set intervals over a set amount of time. These data sets help professionals like data analysts understand trends and changes over time. Weather forecasting, stock price predictions, and traffic reports are all examples of how time series analysis brings value to businesses across various industries.

What is the need for time series analysis?

Time series analysis is crucial because it offers insights that are difficult to obtain through other techniques. This analysis reveals patterns in past behaviors by treating time as a data point, enabling more accurate, data-driven predictions and decisions.

With time series analysis, you can follow the time series data across seasonality, measuring factors such as covariance and autocorrelation. This means you may be able to make decisions that better reflect your company’s trends.

Types of time series analysis

Time series analysis uses non-stationary data, which fluctuates and changes over time. The different types of time series analyses have specific designs and methods to help capture this type of data and include:

• Curve fitting: Plotting data on a curve to identify relationships

• Forecasting: Using previous patterns to predict future trends

• Descriptive analysis: Finding patterns in the data, such as cycles or variations

• Intervention analysis: Demonstrating where an event might change the data

How to do time series analysis

To analyze time series data, you may use different mathematical and statistical formulas to parse the data points you’ve collected and identify trends and patterns. Once you’ve identified the attributes you want to examine, such as seasonality, you may use statistical properties to better understand why the data looks the way it does and how you might repeat or avoid those results.

What are the four components of time series analysis?

To successfully analyze time series data, you may work with these four components:

• Trend: Long-term movement of data, measured either as it increases or decreases

• Cyclicity: Trends that are cyclical but not long-term, such as recessions

• Irregularity: Short-term fluctuations, such as subsidies, that you can’t explain by the cyclical movements in the economy

• Seasonality: Regular occurrences in a time frame of less than a year, with variations of predetermined patterns of behavior

Time series analysis techniques

Three common types of time series analysis techniques are:

• Box-Jenkins multivariate model: In this technique, you track more than one time-dependent variable at a time. These models use a linear analysis function to track several data points, such as humidity and temperature, to see how they may correlate.

• Box-Jenkins ARIMA model: These models assume that data is stationary and only track one data type at a time. The ARIMA model removes any unnecessary data noise, like moving averages and autoregressive terms, to help keep the analysis focused.

• Holt-Winters method: This technique works best for data with trends influenced by the seasons. It’s considered an exponential smoothing technique.

What skills are needed for time series analysis?

Performing time series analysis requires skills in data analysis and statistics, as well as familiarity with programming languages. Python and R are both great options for time series analysis, as they offer statistical packages to help you efficiently execute functions.

Cross-sectional analysis vs. time series analysis

Cross-sectional and time series analyses overlap, but each has uses for different goals. Cross-sectional analysis focuses on a single data point in time, while time series analysis examines that same data point across a range of time.

Cross-sectional data is time series data’s opposite since it’s focused on one moment in time rather than a long range. Time series analysis is unique in that it allows for identifying correlations that might be more difficult to see without the long time range.

Real-world application of time series analysis

Time series analysis has many real-world applications across a variety of industries. For example, businesses that utilize data mining employ time series analysis to identify important consumer trends and patterns within the immense sets of data they collect. Time series analysis can also increase quality control and minimize risk with uses like economic forecasting and earthquake monitoring.

In addition, time series analysis has many important uses in the medical field. For example, it helps diagnose medical conditions through collected long-term data, such as echocardiograms for heart disease patients and electroencephalograms for diagnosing brain-related conditions.

Read more: What Is Health Care Analytics? Definition and Impact

Pros and cons of time series analysis

Time series analysis provides the benefit of finding patterns and trends within huge data sets that would be nearly impossible without the help of a computer and its algorithms. The insight provided by time series analysis means businesses can grow deliberately, making their efforts and strategies more reflective of their reality. It also allows for more accurately identifying future events.

Some challenges with time series analysis include its susceptibility to human error. The ensuing conclusions might be flawed if you choose the wrong model or make incorrect generalizations. In addition, time series analysis requires a lot of data to make accurate predictions. If a business cannot collect, store, and manage immense data sets, it might not find much value in time series analysis since the outputs might not accurately reflect the data.

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