What Is Time Series Analysis?

An important part of business growth is utilizing data. With data, businesses can better understand what works and doesn’t and how to use that information to make the best decisions. One of these data analysis methods 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.

Read on to discover more about time series analysis and its benefits, including its different aspects, real-world examples, pros and cons, and how to get started.

What is the importance of time series analysis?

Time series analysis is crucial because it offers insights that are difficult to obtain through other techniques. By treating time as a data point, this analysis reveals patterns in past behaviors, 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 do you analyze time series data?

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.

Four attributes 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-Winter method: This technique works best for data with trends that are influenced by the seasons. It’s considered an exponential smoothing technique.

Cross-sectional analysis vs. time series analysis

Cross-sectional and time series analyses overlap, but each has uses for different goals. Cross-section 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 such as economic forecasting and earthquake monitoring.

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

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 that businesses can grow in deliberate ways, making their efforts and strategies much 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 using time series analysis since the outputs might not accurately reflect the data.

Learn more with Coursera

Time series analysis is a powerful data strategy for organizations to implement for informed decision-making. You can sharpen your time series analysis skills and learn more about this data analysis technique that might provide value to your business or career with courses and certificates on Coursera. With options such as The State University of New York’s Practical Time Series Analysis course or DeepLearning.AI’s Sequences, Time Series, and Prediction course, you may learn foundational data analysis knowledge and discover a career in this exciting field.