This article discusses cyclical encoding as an alternative to one-hot encoding for time series features in machine learning. Cyclical encoding provides the same information to the model with significantly fewer features.
Learn about the new Amazon time series model, which you can use to forecast energy usage, traffic congestion, and weather.
A powerful library by Amazon — coding example included
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.
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
