During next months I will be working on the book “Displaying time series, spatial and space-time data with R: stories …Continuar leyendo »

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The Time Series Data Library is a collection of about 800 time series that I have maintained since about 1992, and hosted on my personal website. It includes data from a lot of time series textbooks, as well as many other series that I’ve either collected for student projects or helpful people have sent to me. I’ve now moved...

Let’s say you’ve used my Python script to automate the download of a hashtag or search phrase from Twitter (in a Unicode safe way, unlike within R). Now let’s say you want to visualize the number of tweets over time. Easy… Read more ›

A few years ago, I was working on a project where we measured various characteristics of a time series and used the information to determine what forecasting method to apply or how to cluster the time series into meaningful groups. The two main papers to come out of that project were: Wang, Smith and Hyndman (2006) Characteristic-based clustering for...

THIS IS NOT INVESTMENT ADVICE. The information is provided for informational purposes only. In the Time Series Matching post, I used one to one mapping to the compute distance between the query(current pattern) and reference(historical time series). Following chart visualizes this concept. The distance is the sum of vertical lines. An alternative way to map

This is a quick post to address comments raised in the Time Series Matching post. I will show a very simple example of backtesting a Time Series Matching strategy using a distance weighted prediction. I have to warn you, the strategy’s performance is worse then the Buy and Hold. I used the code from Time