Understanding people’s digital behavior is one of the greatest challenges of every business, not to mention predicting it. Approaching the challenge with behavioral analytics gives you a good head start, mainly because it’s analyzing behavior as a sequence of actions over time.
The time dimension is one of the core values of behavioral analytics, enabling us to get a deeper understanding of digital behavior beyond the simple What, Where, and When. By approaching analysis as a sequence of actions over time, we are able to get insights on behavioral patterns such as How content is consumed and shared over time? Why a particular IoT device is used on certain time intervals, or even predict the churn of a casual game, based on learning algorithms of present and past behaviors.
The Practices of Time Series Analysis
A time series, simply put, is a collection of data measured at regular intervals of time. A series of regular data points in chronological order, with evenly spaced gaps between them. Using time series data we can study, and even predict, the nature of the technology that outputs such data, and understand the behavior of the people who use it.
Time Series Analysis, based on values varying in accordance with time is practically used to find “trends” in event based data, Forecast propensity for churn, Predict customer lifetime value or Actions taken over time to realize a behavioral pattern and many more. Adding the the time dimension to the analysis results in deep behavioral insights.
Why Analyze Time Series Data
In the age of Big Data, we crunch through tons of data at speeds like never before. Analysis of data at this scale enables us to extract underlying functions and mathematical relations within a particular data set that would otherwise remain hidden from us. The analytical aspect of time series data is the ability to ask questions or visualize items, that informs us of user behavior over time.
While there are various methods of analyzing time series data, like regression analysis that tests the relations between multiple independent time series at the same point of time, we understand “time series analysis” to be restricted to one single time series at any point in time.
Time series data represents the output or “visible” performance of a system. By analysis of this output, we can often find out the mathematical equivalent of the system, usually in the form of a function; the function takes the inputs of the system and gives us the corresponding output. This is tremendously useful since it lets us study the system and get an idea of how it might perform under various inputs. Alternatively, by analyzing the output of the system, we can find out how the system behaves differently under different input scenarios.
Simulate Real Life Scenarios to Identify and Predict Usage Trends
Time series analysis originally serves mathematical models used to simulate real life systems. Using these models, it is possible to learn how the input data relate to the output produced by the same system. A good example would be of an analysis aiming to plot the temperature of a device that is constantly heated at a steady rate, with respect to time we will get a graph that shows how the temperature has increased over time. Knowing the rate at which both the temperature has risen and that at which heat is being supplied, we can calculate how much heat is required to increase the temperature by a specific unit.
Time series analysis allows us to obtain some previously unknown information about a device and its capacity.
Time series analysis serves a variety of purposes: to identify “trends” or patterns in a data set; to forecast future results depending on past performance; to identify the effects of intervention, that is, how the system output changes if some external event occurs; or to identify what causes deviations in data for control purposes. Additionally, time series data can also be gathered to simply monitor an ongoing process and ensure that it stays within operational limits; this case is purely observational and is not at all concerned with the inner workings of the system. Trying to approximate or “model” the system allows us to attempt to forecast or predict future results, if indeed they are dependent on how the system has performed in the past. Various statistical methods can also be applied to the time series data in order to obtain details about the performance of the system that can then be used for further analysis.
When it comes to people, interaction and the shift from descriptive analysis to predictive insights that answer many what-if scenarios and correlations between products and humans through funnels that will improve performance or find anomalies in different paths, or Cohort report that clearly shows the behavior over time.
Predictive Time Series Analytics
Time series analysis helps to predict future outcomes based on present and past behavior. Often, time series analysis helps us uncover trends that previously went unnoticed; this incident separately might allow us to make successful predictions about the future. However, this should not be confused with the ability to make standalone predictions when there might not exist any tangible reason for that to happen.
Time series analysis, coupled with advances in computation technology, has really opened up an entirely new dimension to us. Being able to sift through tons of data and uncover hidden trends and discover new information about the behavior of the system has allowed us to make unprecedented leaps in optimizing our systems for maximizing the desired performance. At CoolaData, we laid a solid foundation of time series in the way we store the data, analyze it and even opened it to other tools like python and R just for the purpose of building models that are time aware.
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