# ajayarunachalam/Deep\_XF

<https://repo.masters-masters.com/en/repos/ajayarunachalam/Deep_XF>

[Home](https://repo.masters-masters.com/en) › [ajayarunachalam](https://repo.masters-masters.com/en/owners/ajayarunachalam) › [Deep\_XF](https://repo.masters-masters.com/en/repos/ajayarunachalam/Deep_XF)

ajayarunachalam/Deep\_XF has 119 stars on GitHub. Its RepoMasters card: Prototype tier. Package towards building Explainable Forecasting and Nowcasting Models…

| Field | Value |
| --- | --- |
| Owner | [ajayarunachalam](https://repo.masters-masters.com/en/owners/ajayarunachalam) |
| Stars | 119 |
| Forks | 24 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | None |
| Created | 2021 |
| Topics | python, forecasting, nowcasting-models, forecasting-models, explainable-ml, deep-neural-networks, deep-learning, time-series, time-series-analysis, auto-ml |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [ajayarunachalam/Deep\_XF](https://github.com/ajayarunachalam/Deep_XF) |

## Repository

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeser…

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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