Nvidia Researchers developed and open-sourced a standardised machine learning framework for time series forecasting benchmarking.
The introduction of TSPP(Time Series Processing Program), a comprehensive benchmarking tool by researchers from Nvidia, is a major stride in addressing the challenges of creating models that handle complex data features like trends, noise, and evolving relationships. Furthermore, it will offer a standardised approach for evaluating machine learning solutions in real-world scenarios.
TSPP introduces a benchmarking framework that facilitates integrating and comparing various models and datasets. This framework is designed to comprehensively consider every phase of the machine learning lifecycle, from data curation to deployment, ensuring a thorough evaluation and comparison of different methods.
The framework’s modular components allow for the fast and easy integration of datasets, models, and training techniques, a significant advantage over traditional methods.
Traditionally, time series forecasting has relied on methods like Gradient Boosting Machines (GBM) and deep learning models. GBMs are favoured for their effectiveness, especially in competition settings like Kaggle, but they require substantial feature engineering and expertise. Despite their promise, deep learning models have seen less independent use, primarily due to limitations in data availability and the complexity of their implementations.
