BigML’s Spring 2017 Release is here! Join us on Thursday July 20, 2017, at 10:00 AM US PDT (Portland, Oregon. GMT -07:00) / 07:00 PM CEST (Valencia, Spain. GMT +02:00) for a FREE live webinar to discover the updated version of BigML’s platform. We’ll be showcasing Time Series, the latest supervised learning method added to our toolset for analyzing time based data when historical patterns can explain future behavior.
Our new capability brought to the BigML Dashboard, API and WhizzML is Time Series, a well-known supervised learning method commonly used for predicting stock prices, sales forecasting, website traffic, production and inventory analysis as well as weather forecasting, among many other use cases. In BigML, a Time Series model is trained with Time Series data, that is, a field that contains a sequence of equally distributed data points in time. BigML implements exponential smoothing to train Time Series models, where the data is modeled as a combination of exponentially-weighted moving averages.
Time Series is a supervised learning model, as such, it’s ideal to evaluate its performance. As usual, prior to training your model you will need to split your dataset in two different subsets: one for training and the other one for testing. However, the split for Time Series has to be sequential rather than random, which means that you will test your model against the most recent instances in your dataset representing the latter period. BigML offers a special option (via API or Dashboard) for this type of sequential split. You can then easily interpret the results of your model by visually comparing those against the corresponding test data in a chart view.
As in every BigML resource, you can make predictions with your model. With Time Series Forecasts you can easily forecast events in short or longer time horizons. You can also employ a Time Series model to forecast the future values of multiple objective fields. Additionally, BigML offers the ability to generate your forecast in real-time on your preferred local device at no cost, which is an ideal context to make faster predictions.
Are you ready to discover this new BigML resource? Please visit the dedicated release page for further learning. It includes a series of six blog posts about Time Series, the BigML Dashboard and API documentation, the webinar slideshow as well as the full webinar recording.