Our quest for robust time series forecasting at scale
Farzan Rohani · Sep 15, 2017
Date: 2017-09-15
Time: 15:30-16:30
Location: BURN 1205
Abstract:
The demand for time series forecasting at Google has grown rapidly along with the company since its founding. Initially, the various business and engineering needs led to a multitude of forecasting approaches, most reliant on direct analyst support. The volume and variety of the approaches, and in some cases their inconsistency, called out for an attempt to unify, automate, and extend forecasting methods, and to distribute the results via tools that could be deployed reliably across the company. That is, for an attempt to develop methods and tools that would facilitate accurate large-scale time series forecasting at Google. We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. In this talk, we recount how we approached the task, describing initial stakeholder needs, the business and engineering contexts in which the challenge arose, and theoretical and pragmatic choices we made to implement our solution. We describe our general forecasting framework, offer details on various tractable subproblems into which we decomposed our overall forecasting task, and provide an example of our forecasting routine applied to publicly available Turkish Electricity data.