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Forecasting

Using Neural Networks to Build Robust Hourly Models

February 14, 2018

Our first brown bag seminar of the year is entitled, “Using Neural Networks to Build Robust Hourly Models.” Neural networks are flexible functional forms that learn about nonlinear relationships from the data. These models can be used to identify nonlinearities and variable interactions and to construct x variables that embody these relationships. Learn more about these variables and how they can be used in regression models that have rich relationships that are also simple and robust.

Join us on Tuesday, Feb. 20.  To register, for this Brown Bag and other forecasting events, go to this link: http://www.itron.com/forecastingworkshops.

Participation is free, but prior registration is required. Each seminar lasts approximately one hour, allowing 45 minutes for the presentation and 15 minutes for questions. Seminars start at noon PST.  If you cannot attend a seminar or missed one, don’t worry. Your registration ensures that a link to the recording will be sent to you automatically approximately one week after the seminar date.

By Paige Schaefer

Sr Forecast Analyst

Paige Schaefer is a Product Marketing Manager in Itron’s Outcomes group for the strategy, planning and implementation of projects supporting marketing functions spanning electricity, water and gas business units. She interacts directly with sales, product and corporate marketing to identify new marketing opportunities, recommend actions and the coordination of targeted campaigns to increase brand awareness and market share where she works closely with the teams to develop content and strategies. In addition, she provides website support, event coordination and manages Itron’s Energy Forecasting Group (EFG), which supports end-use data development, the Statistical End-use Approach (SAE) and coordinates their annual meeting for discussing modeling and forecasting issues. Paige has a B.S. in Business Administration from San Diego State University with an emphasis in Marketing.