This talk will give introductory information about time series data forecasting and examples of its applications, and then delve into some lessons learnt from working on many time series forecasting topics, examples of the lessons:
- Garbage In, Garbage Out: Emphasizing the critical need for investing in data quality improvement.
- Investing in Exploratory Data Analysis always pays off: The importance of thoroughly understanding data before modelling, using techniques like autocorrelation and decomposition.
- Model Size Doesn't Necessarily Matter: Demonstrating how advanced AI methods can sometimes underperform compared to simpler models.
- Think Bigger: Exploring the benefits of examining relationships between multiple time series for more accurate and cost-effective models.
- Continuous Monitoring Challenges: Addressing the difficulties of evaluating forecasting models with traditional monitoring techniques in highly seasonal data.
These insights aim to provide a comprehensive understanding of the practical aspects and challenges of time series forecasting, offering valuable takeaways for industry professionals.
Mohamed is a data scientist with 10+ years of experience in applying data analytics to solving business challenges, he is part of the Discipline Expert community at Siemens Energy and is leading the Time Series Analytics R&D program.
