Publications by Peter Laurinec
Ensemble learning for time series forecasting in R
Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible overfit. In the domain of time series forecasting, we have somehow obstructed situation because of d...
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TSrepr – Time Series Representations in R
I’m happy to announce a new package that has recently appeared on CRAN, called “TSrepr” (version 1.0.0: https://CRAN.R-project.org/package=TSrepr). The TSrepr package contains methods of time series representations (dimensionality reduction, feature extraction or preprocessing) and several other useful helper methods and functions. Time ser...
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TSrepr use case – Clustering time series representations in R
In the previous blog post, I showed you usage of my TSrepr package. There was shown what kind of time series representations are implemented and what are they good for. In this tutorial, I will show you one use case how to use time series representations effectively. This use case is clustering of time series and it will be clustering of consumer...
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My eRum 2018 biggest highlights
On the range of dates 14.-16. May 2018, the European R users meeting (eRum) was held in Budapest. I was there as an active participant since I had the presentation about time series data mining. The eRum 2018 was a very successful event and I want to thank organizers of this event for a great organization of it. This blog post will be oriented o...
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Multiple Data (Time Series) Streams Clustering
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Bootstrapping time series for improving forecasting accuracy
Bootstrapping time series? It is meant in a way that we generate multiple new training data for statistical forecasting methods like ARIMA or triple exponential smoothing (Holt-Winters method etc.) to improve forecasting accuracy. It is called bootstrapping, and after applying the forecasting method on each new time series, forecasts are then agg...
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Dangerous streets of Bratislava! Animated maps using open data in R
At the work recently, I wanted to make some interesting start-up pitch (presentation) ready animated visualization and got some first experience with spatial data (e.g. polygons). I enjoyed working with such a type of data and I wanted to improve on working with them, so I decided to try to visualize something interesting with Bratislava (Slovaki...
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CoronaDash app use case – Clustering countries’ COVID-19 active cases trajectories
COVID-19 disease spread hit the World really globally and also the field of mathematicians/ statisticians/ machine learning researchers and related. These experts want to help to understand for example future trends (forecast) of the coronavirus spread. My motivation, in this case, was to create interactive dashboard about COVID-19 to inform abou...
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