General¶
Learn about time series analysis
Time series forecasting has long relied on statistical methods and specialized machine learning algorithms. Recently, however, large language models (LLMs) have shown surprising versatility in this domain, thanks to their strong sequence modeling capabilities. In this post, we demonstrate how to use Amazon Chronos, a framework that leverages LLMs for time series tasks, for one-step-ahead forecasting…
Forecasting
September 2, 2024
Building a well-performing machine learning model requires substantial time and resources. Automated machine learning (AutoML) automates the end-to-end process of building, training and tuning machine learning models. This not only accelerates the development cycle, but also makes machine learning more accessible to those without specialized data science expertise…
Classification
August 20, 2024
Forecasting commodity prices is a particularly challenging task due to the intricate interplay of supply and demand dynamics, geopolitical factors, and market sentiment fluctuations. Deep learning models have been shown to be more effective than traditional statistical models at capturing the complex and non-linear relationships inherent in commodity markets…
Forecasting
July 26, 2024
Inflation forecasts are used for informing economic decisions at various levels, from households to businesses and policymakers. The application of machine learning methods to inflation forecasting offers several potential advantages, including the ability to handle large and complex datasets, capture nonlinear relationships, and adapt to changing economic conditions…
Forecasting
March 20, 2024
FRED-MD is an open-source dataset of monthly U.S. macroeconomic indicators maintained by the Federal Reserve Bank of St. Louis. The FRED-MD dataset was introduced to provide a common benchmark for comparing model performance and to facilitate the reproducibility of research results…
Datasets
January 11, 2024