Long-term Time Series Forecasting and Data Generated by Complex Systems
Kaisa Taipale (CH Robinson)
Data science, machine learning, and artificial intelligence are all practices implemented by humans in the context of a complex and ever-changing world. This talk will focus on the challenges of long-term, seasonal, multicyclic time series forecasting in logistics. I will discuss algorithms and implementations including STL, TBATS, and Prophet, with additional attention to the data-generating processes in trucking and the US economy and the importance in algorithm selection of understanding these data-generating processes. Subject matter expertise must always inform mathematical exploration in industry and indeed leads to asking much more interesting mathematical questions.