The growing popularity of IoT, sensor networks, and other telemetry applications 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 talk, we will show how to leverage InfluxDB to implement some solutions to tackle when it comes to time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real-world use case, we will be discussing the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily.
Watch the webinar by clicking on the link at the bottom of this document.