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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...
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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...
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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...
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Multiple Data (Time Series) Streams Clustering
Nowadays, data streams occur in many real scenarios. For example, they are generated from sensors, web traffic, satellites, and other interesting use cases. We have to process them in a fast way and...
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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...
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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...
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CoronaDash app use case - Clustering countries' COVID-19 active cases...
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...
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Multistep horizon loss optimized forecasting with ADAM
Recently, during work on some forecasting task at PowereX, I stumbled upon an interesting improvement in time series forecasting modeling. In general, in regression modeling, we use evaluation/loss...
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Multivariate Regression Ensemble Models for Errors Prediction
In the last blog post about Multistep forecasting losses, I showed the usage of the fantastic method adam from the smooth R package on household electricity consumption data, and compared it with...
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Overview of clustering methods in R
Clustering is a very popular technique in data science because of its unsupervised characteristic - we don’t need true labels of groups in data. In this blog post, I will give you a “quick” survey of...
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