Blog Archives

My eRum 2018 biggest highlights

May 18, 2018
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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...

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TSrepr use case – Clustering time series representations in R

March 12, 2018
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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 representati...

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TSrepr – Time Series Representations in R

January 25, 2018
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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 series representation can be defined as follows: Let \( x \) be a time series of length \( n \),...

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Ensemble learning for time series forecasting in R

October 18, 2017
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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 over...

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Using regression trees for forecasting double-seasonal time series with trend in R

August 21, 2017
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Using regression trees for forecasting double-seasonal time series with trend in R

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 machine learning method for modeling. They are favorite because...

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R<-Slovakia meetup started to build community in Bratislava

March 25, 2017
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R<-Slovakia meetup started to build community in Bratislava

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

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R<-Slovakia meetup started to build community in Bratislava

March 25, 2017
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R<-Slovakia meetup started to build community in Bratislava

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

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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

January 26, 2017
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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in 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 and use it for accurate forecasting. Interactions between two...

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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

January 23, 2017
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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in 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 and use it for accurate forecasting. Interactions between two...

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Forecast double seasonal time series with multiple linear regression in R

December 2, 2016
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Forecast double seasonal time series with multiple linear regression in R

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 as forecast methods. The biggest disadvantage of this approach...

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