From b57ed796a8d775384fe337688d50bd6d14c7cbd5 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Mon, 21 Dec 2020 15:14:55 -0400 Subject: [PATCH 1/2] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 910e85e..fc6ec77 100644 --- a/README.md +++ b/README.md @@ -982,6 +982,8 @@ Twin Systems and Weakly-Supervised Learning](https://ieeexplore.ieee.org/stamp/s - [pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting): A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures +[Flow Forecast](https://github.com/AIStream-Peelout/flow-forecast): A deep learning for series forecasting, classification, and anomaly detection framework with built in hyperparameter searches, experiment tracking controls, and wrappers to easily deploy temporal models to production. + ## Datasets - [A curated list of awesome time series databases](https://github.com/xephonhq/awesome-time-series-database) From 47f1d4554bfc6b43f66c653f9e2521177c06bfa1 Mon Sep 17 00:00:00 2001 From: isaacmg Date: Mon, 21 Dec 2020 15:16:13 -0400 Subject: [PATCH 2/2] add d --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index fc6ec77..f607734 100644 --- a/README.md +++ b/README.md @@ -982,7 +982,7 @@ Twin Systems and Weakly-Supervised Learning](https://ieeexplore.ieee.org/stamp/s - [pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting): A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures -[Flow Forecast](https://github.com/AIStream-Peelout/flow-forecast): A deep learning for series forecasting, classification, and anomaly detection framework with built in hyperparameter searches, experiment tracking controls, and wrappers to easily deploy temporal models to production. +- [Flow Forecast](https://github.com/AIStream-Peelout/flow-forecast): A deep learning for series forecasting, classification, and anomaly detection framework with built in hyperparameter searches, experiment tracking controls, and wrappers to easily deploy temporal models to production. ## Datasets