How Time Series Forecasting Can Help Business Make Better Decisions

This conference talk provides introductory information about time series data forecasting and its business applications, delivered by Mohamed Ahmed, Lead Data Scientist at Siemens Energy. Learn about critical lessons from real-world time series forecasting projects, including the importance of data quality, the value of thorough exploratory data analysis using techniques like autocorrelation and decomposition, why simpler models sometimes outperform advanced AI methods, the benefits of analyzing relationships between multiple time series, and the challenges of evaluating forecasting models with traditional monitoring techniques in highly seasonal data. Gain practical insights from Ahmed's 10+ years of experience applying data analytics to business challenges as part of the Siemens Energy Discipline Expert community and leader of the Time Series Analytics R&D program. Recorded at the 2025 GAIA Conference on April 11 at Svenska Mässan in Gothenburg, Sweden.