2020/06/14
Embedding Many Time Series via Recurrence Plots
We demonstrate how recurrence plots can be used to embed a large set of time series via UMAP and HDBSCAN to quickly identify groups of series with unique characteristics such as seasonality or outliers. The approach supports exploratory analysis of time series via visualization that scales poorly when combined with large sets of related time series. We show how it works using a Walmart dataset of sales and a Citi Bike dataset of bike rides.
2020/06/07
Rediscovering Bayesian Structural Time Series
This article derives the Local-Linear Trend specification of the Bayesian Structural Time Series model family from scratch, implements it in Stan and visualizes its components via tidybayes. To provide context, links to GAMs and the prophet package are highlighted. The code is available here. I tried to come up with a simple way to detect “outliers” in time series. Nothing special, no anomaly detection via variational auto-encoders, just finding values of low probability in a univariate time series.
2020/01/18
The Causal Effect of New Year’s Resolutions
We treat the turn of the year as an intervention to infer the causal effect of New Year’s resolutions on McFit’s Google Trend index. By comparing the observed values from the treatment period against predicted values from a counterfactual model, we are able to derive the overall lift induced by the intervention. Throughout the year, people’s interest in a McFit gym membership appears quite stable.1 The following graph shows the Google Trend for the search term “McFit” in Germany for April 2017 to until the week of December 17, 2017.
2019/06/16
satRday Berlin Presentation
My satRday Berlin slides on “Modeling Short Time Series” are available here. This saturday, June 15, Berlin had its first satRday conference. I eagerly followed the hashtags of satRday Amsterdam last year and satRday Capetown the year before that on Twitter. Thanks to Noa Tamir, Jakob Graff, Steve Cunningham, and many others, we got a conference in Berlin as well. When I saw the call for papers, I jumped at the opportunity to present, trying what it feels like to be on the other side of the microphone; being in the hashtag instead of following it.
2019/04/16
Modeling Short Time Series with Prior Knowledge
I just published a longer case study, Modeling Short Time Series with Prior Knowledge: What ‘Including Prior Information’ really looks like. It is generally difficult to model time series when there is insuffient data to model a (suspected) long seasonality. We show how this difficulty can be overcome by learning a seasonality on a different, long related time series and transferring the posterior as a prior distribution to the model of the short time series.