The latest advancement in Time Series Forecasting from AWS: Chronos(Python Code included)
Citation: @article{ansari2024chronos,author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},title = {Chronos: Learning the Language of Time Series},journal = {arXiv preprint arXiv:2403.07815},year = {2024}}
Full Article: 2403.07815.pdf (arxiv.org)
Time series forecasting is a critical task in various domains, ranging from finance and energy to healthcare and climate science. Traditionally, statistical models like ARIMA and ETS have been the go-to methods for forecasting. However, with the advent of deep learning, there has been a paradigm shift towards leveraging neural network architectures for improved forecasting accuracy. In particular, the rise of large language models (LLMs) has sparked interest in developing foundation models for time series forecasting.
Chronos, a novel framework for pretrained probabilistic time series models. Developed by researchers, Chronos aims to simplify time series forecasting tasks…
