This paper provides a theoretical analysis of Transformers' limitations for time series forecasting through the lens of In-Context Learning (ICL) theory, demonstrating that even powerful Transformers often fail to outperform simpler models like linear models. The study focuses on Linear Self-Attention (LSA) models and shows that they cannot achieve lower expected MSE than classical linear models for in-context forecasting, and that predictions collapse to the mean exponentially under Chain-of-Thought inference.
This article details six practical use cases for Model Context Protocol (MCP) to automate workflows using AI agents and integrations with tools like Slack, Google Calendar, BigQuery, Linear, GitHub, and HubSpot. It highlights the impact of these automations on team efficiency and productivity.
