ajayarunachalam/Deep_XF
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 timeseries signals.
This tool helps you build forecasting and nowcasting models using time-series data to predict future trends or current conditions with detailed explanations. You provide your time-series dataset, and it outputs interpretable predictions for what's next, or what's happening now. It's designed for data scientists, analysts, or researchers who need quick, explainable insights from their time-based information.
118 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to quickly build and understand predictions from your time-series data, like sales forecasts, energy consumption, or economic indicators, without extensive coding.
Not ideal if you primarily need to perform complex custom deep learning research or if your data is not in a time-series format.
Stars
118
Forks
25
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 08, 2022
Commits (30d)
0
Dependencies
13
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