It’s one thing to record data and observe how it has changed between one time and another—it’s another to examine how data changes over time and to analyze shifts in data points at concrete time intervals. Time is a critical part of all data analysis, and it can impact conclusions, forecasts, and other recommendations from data scientists.
To accurately understand the relationship between data shifts and time, data analysts need to understand time series analysis and forecasting. This guide will break down what time series analysis is, the role it plays in forecasting efforts, and certain models data analysts use to perform time series analysis.
Use the following clickable menu to skip ahead to any section:
- What is time series analysis?
- How is time series analysis used in data analytics?
- Time series analysis use cases
- Types of time series analysis
- Time series analysis models and techniques
- Final thoughts
Ready to learn more about time series analysis? Let’s get started!
1. What is time series analysis?
Time series analysis is a method of analyzing data points collected over a set time period. Through time series analysis, data analysts measure various data points at regular intervals. In this way, they can see how the points of data change with the passage of time, instead of gathering data points randomly or intermittently.
Time series analysis is important because it doesn’t just show how different variables can change over time. It also shows how data points adjust over the course of the entire measurement time frame—not just between when data collection begins and when it ends.
Time series analysis usually needs many data points. The more data points collected, the more consistent and reliable the results will be. Furthermore, acquiring more data points reduces the chance that any patterns or trends detected are outliers or unreliable.
When used properly, time series data analysis can be utilized to predict future data performance based on past results.
2. How is time series analysis used in data analytics?
Time series data analysis is used in a variety of contexts. Many organizations leverage time series analysis to understand the causes of different systemic patterns or detected trends over time. When used with a graph or other data visualization element, businesses can see things like seasonal trends and determine:
- Why those trends occur,
- Whether they can affect those trends for better results, and
- How the trends may change or stay the same in the future
In fact, that last focus is why most organizations leverage time series analysis in the first place. Through acquiring and analyzing enough data, businesses and other organizations can perform time series forecasting. They can predict the likelihood or possibility of future events.
3. Time series analysis use cases
Let’s take a look at a few use cases or examples of time series analysis in action.
One great example of time series analysis and action is the examination of the stock market. Automated trading algorithms and experienced stock market traders alike both perform time series analyses on stocks, bonds, and other financial vehicles.
They attempt to look at how stocks and other assets change in value over time so they can predict the value of those assets in the future. This is highly complex and is far from 100% reliable; after all, there’s a reason investing in the stock market is never a sure thing.
Still, time series analysis is part of what allows trading algorithms to predict stock market movements with some reliability. Algorithms designed by expert coders are also leveraged in industries such as in healthcare, finance, and so on.
Another good example is meteorology or climatology. Climate specialists look at concrete data points such as sea levels, recorded temperatures, humidity levels, and more. Then they compare those data points to how they have changed over time by collecting the data at the exact same time each week, month, or year.
Through these collection efforts, climate specialists can leverage time series analysis to determine:
- How the weather is changing
- Whether certain effects or others are responsible for changing weather patterns
- How the weather may change or evolve in the future
- … and many other things!
In both of these examples, time series analysis is an important method that data analysts must incorporate into their strategies to understand the full picture.
From large businesses to micro businesses, or businesses with ten employees or fewer (which make up 92% of all businesses in the United States), time series analysis is a vital tool useful for understanding markets, business limitations, finances, and more.
4. Types of time series analysis
Data analysts often use different types of time series analyses rather than constructing overly complex or crowded charts and graphs. Overfitting models or overly complex models may produce a lack of fit or may make it difficult for analysts to distinguish between real relationships and random errors.
To that end, some of the most common times series analysis models include:
- Exploratory analysis models, which highlight and focus on primary features of the time series data in visual formats
- Classification models, which both identify and assign categories to data points
- Descriptive analyses, which identify patterns in time series data such as cycles or trends
- Explanatory analyses, which explore and understand the relationships of data within collected data sets (such as cause and effect)
- Forecasting analysis models, which are used to predict future data performance. Forecasting is always based on historical trends
5. Time series analysis models and techniques
There are many different models and techniques that can be used to perform time series analyses. Let’s explore three of the most common models now.
Box-Jenkins ARIMA models
Box-Jenkins ARIMA models are univariate analysis models leveraged to understand one time-dependent variable, such as student performance over time, temperature over time, et cetera. ARIMA models are further used to predict future data points of those single, time-dependent variables.
To use the ARIMA model properly, data analysts must both account for and then remove seasonalities or other differences in past data points as much as possible. ARIMA models include terms that allow analysts to account for things like seasonal differences, moving averages, and other terms within the model so they can compensate for these effects.
Box-Jenkins multivariate models
Box-Jenkins multivariate models are, as the name suggests, helpful for analyzing more than one time-dependent variable. In this way, data analysts can examine how multiple variables change over time and potentially determine how those variables influence each other’s development.
One great example is temperature and humidity in meteorology. Meteorologists may use multivariate models to determine how temperature impacts humidity in local climates or vice versa. They can see how both of these effects change over time with the seasons and how time impacts their effects on each other.
The Holt-Winters method is a useful time series analysis technique that relies on exponential smoothing. In short, the Holt-Winters method helps data analysts predict outcomes or future events. This method includes seasonality and cyclic data effects, thereby improving the accuracy of data forecasts.
6. Final thoughts
Ultimately, time series analysis is an important part of data analytics, particularly when predicting future events or making wise business decisions. Data analysts need to know how to perform time series analysis to better serve their clients and to understand how data points can evolve more thoroughly than by simply observing beginning and end data points on a graph.
Want to learn more about time series analysis and data analytics in general? Why not start by signing up for this free, 5-day, data analytics short course? Alternatively, to learn more about other data analytics topics, be sure to check out the following introductory articles: