Learn about the new Amazon time series model, which you can use to forecast energy usage, traffic congestion, and weather.
- A machine learning framework developed by Monash University and Ant Group.
TIME-LLM repurposes Large Language Models (LLMs) for time series forecasting without modifying their core structure.
- The innovative reprogramming technique called Prompt-as-Prefix (PaP) translates time series data into text prototypes, allowing LLMs to interpret and predict time series data accurately.
TIME-LLM demonstrates superior performance in both few-shot and zero-shot learning scenarios compared to specialized forecasting models across various benchmarks.
The success of TIME-LLM opens up new avenues for applying LLMs in data analysis and beyond, as it shows that they can be effectively repurposed for tasks outside their original domain.
Explore the architecture of TiDE and apply it in a forecasting project using Python
Nvidia Researchers Developed and Open-Sourced a Standardized Machine Learning Framework for Time Series Forecasting
Nvidia researchers have developed and open-sourced a standardized machine learning framework called TSPP (Time Series Prediction Platform) for time series forecasting. The framework is des
igned to facilitate the integration and comparison of various models and datasets, covering all aspects of the machine learning process from data handling to model deployment.
The TSPP framework includes critical components like data handling, model design, optimization, and training, as well as inference, predictions on unseen data, and a tuner component that s
elects the top configuration for post-deployment monitoring and uncertainty quantification. The methodology of TSPP is comprehensive, covering all aspects of the machine learning process.
Building on ideas from Meta’s Prophet package to create powerful features for time series machine learning models
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