RapidMiner's New Time Series Extension

I'm really liking the overhaul of the old Value Series extension. RapidMiner has been building a new Time Series extension that started with the great ARIMA operator. Now they're adding new Windowing and Sliding Window operators. My notes below...

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Learn RapidMiner Livestream Volume 3

My latest livestream. In this episode I continue with the Word2Vec process and build a synomym stemming dictionary. Then I talk about how to do time series in RapidMiner. I explain the Windowing operator, the Sliding Window Validation operator...

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Using SVM Kernels for Time Series Analysis

I wanted to share two research papers that are invaluable to anyone trying to use Support Vector Machines (SVM) for modeling the stock market. One written by an author well known to the Rapid-I team, and another by Korean researcher. I've used...

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Building an AI financial market model - Lesson IV

There are NEW livestream videos about RapidMiner! Visit my Channel here In Lesson 3, I introduced the Sliding Window Validation operator to test how well we can forecast a trend in a time series. Our initial results are very poor, we were able to...

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Building an AI financial market model - Lesson III

There are NEW livestream videos about RapidMiner! Visit my Channel here In Lesson 2, I went over the concept of MultiObjective Feature Selection (MOFS). In this lesson we’ll build on MOFS for our model but we’ll forecast the trend and measure...

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Building an AI Financial market model - Lesson II

There are NEW livestream videos about RapidMiner! Visit my Channel here In this tutorial I want to show you how to use MultiObjective Feature Selection (MOFS) in RapidMiner. It’s a great technique to simultaneously reduce your attribute set and...

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Building an AI financial market model - Lesson I

There are NEW livestream videos about RapidMiner! Visit my Channel here Before you can begin with building your own AI Financial Market Model (machine learned), you have to decide on what software to use. Since I wrote this article in 2007, many...

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