Time Series

An older link from April 2017 that I believe became AutoGluon. AutoGluon is fantastic for time series and a host of other AutoML use cases. So, what models do we include in our ensemble? Pretty much any reasonable model we can get our hands on! Specific models include variants on many well-known approaches, such as the Bass Diffusion Model, the Theta Model, Logistic models, bsts, STL, Holt-Winters and other Exponential Smoothing models, Seasonal and other other ARIMA-based models, Year-over-Year growth models, custom models, and more. Indeed, model diversity is a specific objective in creating our ensemble as it is essential to the success of model averaging. Our aspiration is that the models will produce something akin to a representative and not overly repetitive covering of the space of reasonable models. Further, by using well-known, well-vetted models, we attempt to create not merely a “wisdom of crowds” but a “wisdom of crowds of experts” scenario, in the spirit of Mannes [6]. ...

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 and show how to insert a bit of R code to deseason a time series. I’m going to change the time for the next Live Stream. Stay tuned for next livestream on 5/25/18 at 12PM EDT. ...

** 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 forecast the trend with an average accuracy of 55.5%. This is fractionally better than a simple coin flip! In this updated lesson I will introduce the ability of Parameter Optimization in RapidMiner to see if we can forecast the trend better. ...

** 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 it’s accuracy. Revisiting MOFS We learned in lesson 2 that RapidMiner can simultaneously select the best features in your data set while maximizing the performance. We ran the process and the best features were selected below. ...

** 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 maximize your performance (hence: MultiObjective). This feature selection process can be run over and over again for your AI Financial Market Model, should it begin to drift. Load in the Process from Tutorial One Start by reading the Building an AI Financial Market Model – Lesson 1 post. At the bottom of that post you can download the RapidMiner process. ...

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 new advances have been made in machine learning. Notably the python module Scikit Learn came out and Hadoop was released into the wild. I’m not overly skilled in coding and programming – I know enough to get by- I settled on RapidMiner. RapidMiner is a very simple visual programming platform that let’s you drag and drop “operators” into a design canvas. Each operator has a specific type of task related to ETL, modeling, scoring, and extending the features of RapidMiner. ...