Articles by Peter Laurinec

Multiple Data (Time Series) Streams Clustering

February 2, 2019 | 0 Comments

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 extract from them as much knowledge as we can. Data s... [Read more...]

My eRum 2018 biggest highlights

May 18, 2018 | 0 Comments

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 ...
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TSrepr – Time Series Representations in R

January 25, 2018 | 0 Comments

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 ...
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Ensemble learning for time series forecasting in R

October 18, 2017 | 0 Comments

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...
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Forecast double seasonal time series with multiple linear regression in R

December 2, 2016 | 0 Comments

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 ...
[Read more...]

Forecast double seasonal time series with multiple linear regression in R

December 2, 2016 | 0 Comments

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 ... [Read more...]

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