#time series forecasting

Discover Chronos' use of language model architectures to improve time series forecasting precision and efficiency. Transform time series data to token sequences for powerful probabilistic predictions. Chronos integrates with AutoGluon, facilitating deployment and advanced analytics without exaggerated claims. Benefit from optimized inference and comprehensive datasets available on HuggingFace, underpinned by Amazon's innovative machine learning research. Ideal for researchers and analysts seeking improved forecasting tools.

Flow Forecast is an open-source deep learning framework for time series forecasting, implementing cutting-edge models such as transformers and attention mechanisms. It features easy cloud integration, provides model interpretability metrics, and supports model serving. The framework accommodates models including LSTM, GRU, and Transformer XL, suitable for classification, anomaly detection, and various forecasting tasks. Managed by Task-TS from CoronaWhy, it offers comprehensive tutorials and detailed documentation.

Leverage advanced deep learning techniques for probabilistic time series modeling using GluonTS, a Python package compatible with PyTorch and MXNet. Enhance forecasting accuracy by integrating pretrained models like Chronos easily through pip. Suitable for researchers and data scientists looking to refine their forecasting capabilities, it includes extensive tutorials and documentation to support contribution to this open-source project.

Time-LLM reprograms large language models to perform time series forecasting by converting time series data into text representations and using domain-specific prompts. This approach supports various LLM backbones including Llama-7B, GPT-2, and BERT. Notably, it has been integrated into the NeuralForecast platform and applied in renewable energy forecasting.

Discover how the iTransformer employs an Inverted Transformer design to improve multivariate time series forecasting without altering traditional modules. This method efficiently captures multivariate correlations by treating time series as variate tokens, resulting in superior predictive performance. Notable outcomes include enhanced accuracy on Alipay's platform and effective zero-shot forecasting capabilities. iTransformer exemplifies improved performance with its efficient attention mechanisms, suitable for various forecasting challenges. Researchers and practitioners can install and utilize iTransformer easily via pip to advance their forecasting techniques.

Prophet is an open-source tool by Facebook’s Core Data Science team for forecasting time series data. Utilizing an additive model, it adjusts for yearly, weekly, and daily seasons and holiday impacts. Prophet is suitable for time series with seasonal trends and incomplete data, available for both Python and R via CRAN and PyPI.