Pre-trained foundation models are making time-series forecasting more accessible and available, unlocking its benefits for smaller organizations with limited resources.
The article "3 forecasts about time series forecasting" from InfoWorld discusses the evolving landscape of time series forecasting, a critical area in data science and analytics. It highlights three key trends: 1) Increased Automation - where machine learning and AI are being used to automate the forecasting process, reducing the need for manual model selection and tuning, thereby making forecasting more accessible to non-experts. 2) Integration with Business Intelligence (BI) Tools - forecasting capabilities are becoming embedded within BI platforms, allowing for real-time analytics and decision-making directly from business dashboards. 3) Enhanced Explainability - as models become more complex, there's a growing demand for transparency in how forecasts are generated, leading to advancements in explainable AI to ensure that business users can understand and trust the predictions. The article emphasizes that these trends are driven by the need for businesses to react quickly to market changes and to make data-driven decisions with greater confidence.
Science & Technology
Mon, January 6, 2025 
