Publications by Peter Laurinec
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 various clustering methods applied to synthetic but also real datasets. What is clustering? Cluster analysis or clustering is the task of grou...
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Enernoc smart meter data – forecast electricity consumption with similar day approach in R
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 addition to time series of electricity consumption, we can have extra information about the consume...
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Enernoc smart meter data – forecast electricity consumption with similar day approach in R
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 addition to time series of electricity consumption, we can have extra information about the consume...
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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 foreca...
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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 foreca...
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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 accu...
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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 accu...
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R<-Slovakia meetup started to build community in Bratislava
On 22. March a first special R related meetup called R took place. As the name of the meetup group implies, it is based in Slovakia, in its capital – Bratislava. I am very happy to be the first speaker on this event ever. R has the intention to build a community to share knowledge of people who are dealing with data science, machine learning or...
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R<-Slovakia meetup started to build community in Bratislava
On 22. March a first special R related meetup called R took place. As the name of the meetup group implies, it is based in Slovakia, in its capital – Bratislava. I am very happy to be the first speaker on this event ever. R has the intention to build a community to share knowledge of people who are dealing with data science, machine learning or...
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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 m...
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