1/4/2024 0 Comments Rapidminer studio prediction![]() Sending the entire dataset into R is very easy. We may want to select only some of the attributes to pass through to R for forecasting or the entire data set. Once this data is read into RapidMiner Studio using any of the available tools, we need to pass the data to R for analysis. There are 77 samples which include data up to November 2013 and we want to forecast these numbers for the next 12-24 months. Our simple time series data consists of 4 columns: a date and 3 numerical quantities which represent monthly sales volumes of three different products. Once you understand this simple but important aspect, then R essentially becomes another powerful “operator” within the vast library of existing RapidMiner Studio operators. In this article, we will expore this second mechanism in a little more detail using the example of a time series problem. The key is to understand how to pass data from RapidMiner Studio to R and back. But the second option requires some initial planning. The first option is fairly easy to put into work, assuming you have successfully added the R extension to RapidMiner. ![]() A more powerful full integration of R capabilities within the RapidMiner Studio process design perspective.An interactive console, similar to the native R console and somewhat less sophisticated than RStudio.RapidMiner integrates really well with R by providing two mechanisms: There are many packages and libraries in R, specifically tailored to handle time series forecasting in the “traditional” manner. ![]() This is done with the help of RapidMiner’s truly flexible integration with the other most popular open source data mining tool, R. For people who do not want to give up the traditional way of doing time series forecasting, have no fear, RapidMiner Studio will allow you to keep your conventional methods by allowing you to fully integrate with standard methods. There are certain aspects of RapidMiner Studio which are “non-conventional,” particularly for time series forecasting. While basic time series forecasting tools, such as exponential smoothing are available as built-in operators, handling advanced techniques like ARIMA, requires some extensive workarounds. Basically, one has to become very conversant with the Windowing operator and other “Series” extension tools, about 80+ different ones. Handling time series forecasting in a tool like RapidMiner requires advanced skills.
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