A Python implementation of the Dynamic Factor Model (DFM) for macroeconomic nowcasting, extending the FRBNY framework (Qian & Bok) with a modern API: real-time vintage management, Kalman-based news decomposition, optional Numba acceleration, caching, and interactive Plotly visualizations.
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.
