Search Results for "budgeting"

PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with...

...Bifrost supports features such as automatic fallback (failover between providers), load balancing across API keys/providers, and semantic caching to reduce latency and cost. It also includes observability with built-in metrics, tracing, logging, and supports governance features like rate limiting, access control, and cost budgeting. The architecture is modular: there is a core engine, plugin layers, and transport layers (HTTP APIs).

NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in...

QChartist is a free and open source technical analysis charting software. Its purpose is to provide a complete set of tools to perform technical analysis on charts and data. It helps to make forecasts mainly for markets but can also be used for weather or any quantifiable data. The program is flexible and its functionalities can be easily extended. You can draw geometrical shapes on your charts or plot programmable indicators from your data. It is also possible to filter or merge data. I got...

Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series...