Design the Most Useful Time Periods for Your Conversions

Time series analysis requires you to batch your data into time periods. If you are visualizing something that happens daily, the “day” may be your time period. But smaller time periods will make your line graph more zig zaggy, and depending upon the underlying data, that can be hard to interpret. On the other hand, batching the data into time periods that are too large will obscure nuances in trends. This blog post will give you guidance about selecting time periods for your time series analysis.

Example Dataset

The United Kingdom (UK) government has promoted the use of electric vehicles (EVs), and as part of that, they have made a lot of data available on the use of public charge points (CPs) for EVs. I took a sample of 1,000 sessions of CP data from December 2024, and aggregated them by CPs. I could tell how many CPs had sessions each day in December, and how many CPs had sessions in each week in December.

This is the structure of raw charge point data.

Time Periods Experimentation

I used Microsoft Excel to illustrate the time periods concept. The goal of the time series analysis was to see if I could identify a trend in usage of CPs for sessions in December 2024. I first used day as the time period, and produced this chart. Here is the “by day” chart.

The chart is very zig zaggy, and it seems to be trending in a downward direction. However, that’s not totally obvious. For example, the number of CPs with sessions on December 18 is very close to the numbers seen at the beginning of the month.

So I experimented by trying to redo the chart by week rather than by day. Here is the “by week” chart.

A time series chart looks different if you graph the data by week rather than by day.

When thinking of time periods, using weeks has the advantage of obviously demonstrating the downward trend. However, the nuances in the daily data are lost.

The trick is to experiment. In this simple demonstration, we just have days and weeks. If you have yearly data, you can also look at aggregating by quarters, by season, or by year. You can make multiple charts, or an interactive chart online that you can adjust with parameters. Your goal of trying different time periods is to identify the visualization that really speaks to you, and helps you understand the trends in your data.

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Time periods are important when creating a time series visualization that actually speaks to you! Get advice on my blog.

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