The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points.
In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily.
Marcello Tomasini, Sr. Data Scientist - Veritas Technologies
Rafael Knuth, Contributing Editor - Data Science Central