time series on Ethan Rosenthalhttps://www.ethanrosenthal.com/tags/time-series/Recent content in time series on Ethan RosenthalHugo -- gohugo.ioen-USMon, 18 Feb 2019 00:00:00 +0000Time Series for scikit-learn People (Part III): Horizon Optimizationhttps://www.ethanrosenthal.com/2019/02/18/time-series-for-scikit-learn-people-part3/Mon, 18 Feb 2019 00:00:00 +0000https://www.ethanrosenthal.com/2019/02/18/time-series-for-scikit-learn-people-part3/In my previous posts in the “time series for scikit-learn people” series, I discussed how one can train a machine learning model to predict the next element in a time series. Often, one may want to predict the value of the time series further in the future. In those posts, I gave two methods to accomplish this. One method is to train the machine learning model to specifically predict that point in the future.Time Series for scikit-learn People (Part II): Autoregressive Forecasting Pipelineshttps://www.ethanrosenthal.com/2018/03/22/time-series-for-scikit-learn-people-part2/Thu, 22 Mar 2018 00:00:00 +0000https://www.ethanrosenthal.com/2018/03/22/time-series-for-scikit-learn-people-part2/In this post, I will walk through how to use my new library skits for building scikit-learn pipelines to fit, predict, and forecast time series data. We will pick up from the last post where we talked about how to turn a one-dimensional time series array into a design matrix that works with the standard scikit-learn API. At the end of that post, I mentioned that we had started building an ARIMA model.Time Series for scikit-learn People (Part I): Where's the X Matrix?https://www.ethanrosenthal.com/2018/01/28/time-series-for-scikit-learn-people-part1/Sun, 28 Jan 2018 00:00:00 +0000https://www.ethanrosenthal.com/2018/01/28/time-series-for-scikit-learn-people-part1/When I first started to learn about machine learning, specifically supervised learning, I eventually felt comfortable with taking some input $\mathbf{X}$, and determining a function $f(\mathbf{X})$ that best maps $\mathbf{X}$ to some known output value $y$. Separately, I dove a little into time series analysis and thought of this as a completely different paradigm. In time series, we don’t think of things in terms of features or inputs; rather, we have the time series $y$, and $y$ alone, and we look at previous values of $y$ to predict future values of $y$.