How Time Series Became a Valuable Analytic


Time series graphs are intuitive, helping you relate a metric to time.

Marketing analysts are often faced with choosing a data visualization that speaks to managers and colleagues interested in advanced insights from their data yet requires a better understanding of the statistics that make those insights useful.

The key to reaching those managers without overwhelming them with machine learning minutiae is leveraging a basic graphic as a simple introduction to concepts. 

Time series graphs with applicable data can be that introduction. They can be a terrific starting point for discussing machine learning projects. 

How Time Series Became a Valuable Analytics Report

Time series graphs are very intuitive. They help you relate a metric to time. Most business managers appreciate this perspective, because it allows them to examine operational performance, using a visual such as a line graph showing how revenue in each quarter has increased or decreased.

Time series graphs are typically seen within social media analytics and web analytics dashboards. No matter which campaigns you’ve worked on, you have likely seen time series data from web analytics solutions or within social media analytics reports.

For example, in a web analytics solution like Google Analytics, you would look at the time series results in a referral traffic report to see what sources are consistently sending traffic to a website. 

Discovering how many clicks an ad campaign received or how many website visits occurred in a sales period is a basic question that a time series graph can answer.

The reports are designed to make metric changes with time visually intuitive. You can gain a sense of whether a particular metric is increasing or decreasing within a time period, such as a percentage or a comparison of discrete numbers. 

But time series graphs in most user-friendly analytics solutions have a fatal flaw. If you needed to know how sustainable a trend is, statistical details are not immediately visible in the tools. 

This means the data spikes and declines appear without showing the influences that caused them. How those surface trends are interpreted make or break major forecasting decisions. 

For example, if a type of car in a market is showing sales growth every quarter, then as a car manufacturer, I would want to know if that sales data trend is sustainable to justify investment in a plant or to enter a manufacturing partnership to produce a vehicle for that market.

Establishing sustainability is especially critical for using time series data as training data for machine learning forecast models. 

The demand for time series forecasting occurs frequently among retailers like Walmart and Target. Retailers must track product shipment from their distribution centers to their stores, so even a small improvement in demand forecasting of their products can cut costs and enhance product availability as part of the customer experience. 

Related Article: The Switch to Google Analytics 4 Is Fast Approaching: Here’s What to Do

How to Start an Advanced Time Series Analysis

To get a sense of a trend and sustainability, you need more statistical analysis. Doing so identifies the right trend within the data. Time series data always contain noise — dips and spikes, which get incorporated into a templated graph. 

Dips and spikes do not always reflect an overall significant trend change in traffic behavior, however. So, you need to separate the data’s true trending signal from the noise that masks it. 

For example, financial market analysts rely on advanced time series models to extract trends from rapidly changing prices and volume of stocks and commodities.

A statistical time series analysis answers two questions:

  1. Does the trend in the dataset indicate a steady pattern?

  2. Does the trend in the dataset correlate only to the given time period?

The answers are extremely important if the data is being used in a regression- or a machine-learning forecasting model.

You can create this kind of statistical analysis using R programming or Python. In fact, R specializes in time series and geographic data. It treats time series data as a special kind of programming object.

An object is essentially a container that holds the data you want to have calculated and can be read by a given language. In R programming the data object is a mathematical vector. 

Time series data is treated as a special version of an R vector that converts different data types into a convenient format for forming graphs and conducting analyses.

To answer the first question, the indication of a pattern, you need to import the data into a tool or program to assess the time series trend from a statistical perspective. 



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