dream-faster/finml-utils

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Created 11 months ago
Updated 9 months ago

dream-faster/finml-utils

dream-faster

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Utility functions of all of our Financial Machine Learning projects

fold

🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This is an internal project - documentation is not updated anymore and substantially differ from the current API.

krisi

⏳ Evaluation of Time-Series Predictions with powerful pdf and web Reporting. Tailored for evaluation of metrics over time!

fold-wrappers

🧱 Wrappers for 3rd party models to be used with fold (https://github.com/dream-faster/fold)

fold-models

⚙️ Speedy implementations of TS Models, built specifically for Fold (https://github.com/dream-faster/fold)

drift-archive

📈 And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series. Includes feature selection and meta labelling.

modular-pipelines

🐾 A lightweight & extensible library to create complex multi-model and multi-modal pipelines, including ``Ensembles`` and ``Meta-Models``

dream-faster-landing

🏔 This is the landing page for Dream Faster Studio

benchmarks

📐 Time series Benchmarks ran on public Datasets with Fold and Krisi across multiple domains.

laplace-gnn-recommendation

⛓️ laplace is an end-to-end ML framework to train and predict on neurally-enhanced graphs for recommendation. The pipeline is designed for self-supervised edge prediction on heterogenous graphs.

research-neural-symbolic-regression