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 various clustering methods applied to synthetic but ...

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 benchmarks. Since I computed predictions from 10 method...

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 metrics based on one step ahead (one value) horizon. B...

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...

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 bootstra...

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 ...

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 representati...

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 series representation can ...

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 over...

After blogging break caused by writing research papers, I managed to secure time to write something new about time series forecasting. This time I want to share with you my experiences with seasonal-trend time series forecasting using simple regression trees. Classification and regression tree (or decision tree) is broadly used ...

On 22. March a first special R related meetup called R

As I wrote in the previous post, I will continue in describing regression methods, which are suitable for double seasonal (or multi-seasonal) time series. In the previous post about Multiple Linear Regression, I showed how to use “simple” OLS regression method to model double seasonal time series of electricity consumption ...

I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were introduced and a forecast method using similar day approach was proposed. ARIMA and exponential smoothing (common methods of time series analysis) were used ...

Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. Smart grids can contain of millions of smart meters, which produce a large amount of data of electricity consumption (long time series). In add...