**FYI: The Power BI August Newsletter just announced Python compatibility. We're looking forward to digging into that in future posts. You can find the newsletter here.**

First, let's talk about what time series data is. Simply put, anything that can be measured at individual points in time can be a time series. For instance, many organizations record their revenue on a daily basis. If we plot this revenue as a line across time, we have time series data. Often, time series data is measured at regular intervals. Weekly measurements are one example of this. However, there are many cases where irregular time intervals are used. For instance, calendar months are not all equal in size. Therefore, a time series of this data would have irregular time intervals. This isn't necessarily a bad thing, but it should be considered when doing important analyses. You can read more about time series data here.

Now, let's talk about Time Series Decomposition. Time Series Decomposition is the process of taking time series data and separating it into multiple underlying components. In our case, we'll be breaking our time series into Trend, Seasonal and Random components. The Trend component is useful for telling us whether our measurements are going up or down over time. The Seasonal component is useful for telling us how heavily our measurements are affected by regular intervals of time. For instance, retail data often has heavy yearly seasonality because people buy particular items at particular times of year, especially during the holidays. Finally, the Random component is what's left over when we remove the Trend and Seasonal components. You can read more about this technique here and here.

Let's hop into Power BI and make a quick time series chart. We'll be using the same Customer Profitability Sample PBIX from the previous posts. You can download it here. If you haven't read Getting Started with R Visuals, it's recommended that you do so now. Let's start by making a simple line chart of Total Revenue by Month.

Total Revenue by Year |

Import From Marketplace |

Power BI Visuals |

Time Series Decomposition Chart |

Time Series Decomposition Chart Description |

Add Time Series Decomposition Chart |

Change to Time Series Decomposition Chart |

Enable Script Visuals |

If you get this error, you need to install the zoo and proto R packages. The previous post walks through this process. You may need to save and reopen the PBIX after installing the packages to see the chart.

Time Series Decomposition of Total Revenue by Month |

View Trend |

Trend |

Seasonality |

Remainder |

Clean |

We could spend all day looking at all the data available here. Instead, let's end by looking at one final aspect of this chart, Algorithm Parameters.

Algorithm Parameters |

Clean (Degree) |

Hopefully, this post opened your eyes just a little to the possible of performing time series analysis within Power BI. The custom visuals in the marketplace provide a strong "middle ground" offering that makes advanced analyses possible outside of hardcore coding tools like R and Python. Stay tuned for the next post where we'll be talking about Forecasting. Thanks for reading. We hope you found this informative.

Brad Llewellyn

Senior Analytics Associate - Data Science

Syntelli Solutions

@BreakingBI

www.linkedin.com/in/bradllewellyn

llewellyn.wb@gmail.com