Why You Should Switch to Time Series Analysis
If you answered yes to all of the below, you should switch to time series analysis;
Does your business of work with humans?
Would you like to understand them? Influence them?
Do you use data for that?
Most people in the business of serving humans or crunching data will need to consider where their analytics stand in their use of time as a metric. This article is an attempt to touch on the paper Time series analysis for psychological research: examining and forecasting change. by Jebb, Tay, Wang, and Huang. My goal is to give you just enough of a taste that you’ll read the longer paper directly.
Track Time – It’s more human
If you study human behavior, you need to take time into account. Time and human behavior are inseparable. As data analytics has advanced, scientists have gathered more evidence that this is true.
Psychological processes are inherently time-bound, and it can be argued that no theory is truly time-independent (Zaheer et al., 1999).
The passage of time has also brought us new and wonderful ways to act on this knowledge. The authors explain how a confluence of trends, such as access to large quantities of time-stamped data, make it easier than ever to take this approach to data analytics and statistics. Therefore all scientists should use time series analytics provide richer, more accurate views into how our world and its humans behave.
Time as the Cause of Death
“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”
-Quote attributed to Sir Ronald Fisher
Unfortunately, the use of time series analysis in psychology hasn’t become mainstream. Like the statistician in the quote, they instead continue to rely on methods of the past. That means collecting smaller amounts of data very close together. We stop collection when we think we’ve reached enough observations to answer a specific question, say 20–50 data points. The entire collection of data is then analyzed and treated as if they all took place at the exact same moment in time. This approach is designed to explain what happened in the past, and why in a limited way.
Predict the Future with Time
Compared to the old methods, time series analysis offers everything that batch processing does and much more. We can still look backward to understand why a thing happened in the past. In addition to that, time series analysis makes it possible to monitor events and trends in real time, to understand things as they happen in the present. The best part of time series analysis is the ability to predict what will happen in the future. Its application in forecasting is why it’s so popular in econometric, financial, and atmospheric studies.
If you think about it, the only reason data is collected and analyzed is to use information about the past to affect the future. So, the better approach is to treat data as a function of time.
Sadly, data is sometimes collected in a way that doesn’t lend itself to time series analysis. It works fine for analyzing the data in a single point in time, but it’s impossible to take out the noise of seasonal trends or make any predictions. That’s why it’s important to approach data collection with the analysis in mind. Understanding and influencing human behavior starts with good data.
Further Reading on Time Series Analytics
If you found this an interesting read, then please check out this wonderful paper. The title of the paper is Time series analysis for psychological research: examining and forecasting change. The authors of the paper are Andrew T. Jebb, Louis Tay, Wei Wang, and Qiming Huang.
Even though this paper was intended for academics and those in “psychological research” I hope a broader audience will read it, especially if your job involves employee and customer culture.
Also, if you are in the world of startups, marketing, or human resources, and time series analysis is something you do or are thinking about for your organization, please let me know! You can reach me on Twitter, acornanalytics.com or the comments below.)
There will be more blog posts related to this topic, but if you have specific verticals or requests please leave them in the comments.