Research

Peer-reviewed papers and citable software artifacts. My work centers on state-space models, spectral decomposition of non-stationary time series, and the data infrastructure that makes reproducible econometric research possible.

Work	Venue	Year	Type
Non-Stationary Spectral Decomposition Network: Adaptive Spectral Emission Heads and Frequency Modulation	IEEE-ORSS 2026	2026	Paper
Non-Stationary Spectral Decomposition Network for Econometric Time Series Forecasting	FLAIRS-39	2026	Paper
fedfred: A Python client for the Federal Reserve Economic Database (FRED) API	Zenodo	2025	Software

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Non-Stationary Spectral Decomposition Network: Adaptive Spectral Emission Heads and Frequency Modulation

Nikhil Sunder · 2026 IEEE Opportunity Research Scholars Symposium (ORSS) · IEEE Xplore · 2026

The second paper in the NS-SDN arc. The FLAIRS paper established that a recurrent latent state can emit interpretable amplitude, instantaneous-frequency, and phase trajectories through a single spectral emission equation; this work treats the emission mechanism itself as the design variable. It replaces the monolithic emission map with adaptive spectral emission heads — separate, state-conditioned parameterizations for amplitude, frequency, and phase — and introduces explicit frequency modulation so that instantaneous frequency is driven directly by the latent dynamics rather than recovered implicitly from phase increments. The result is a decomposition whose frequency content can shift within a regime, not just between regimes, which is the property a nonstationary econometric series actually demands. [One sentence on the empirical result: series used, comparison against NS-SDN-I, and what the modulated emission buys in forecast error or spectral coherence.]

Read on IEEE Xplore · Code

Citation (BibTeX)

@inproceedings{Sunder_2026_ORSS,
    title     = {Non-Stationary Spectral Decomposition Network: Adaptive Spectral Emission Heads and Frequency Modulation},
    author    = {Sunder, Nikhil},
    booktitle = {2026 IEEE Opportunity Research Scholars Symposium (ORSS)},
    publisher = {IEEE},
    year      = {2026},
    pages     = {},
    doi       = {10.1109/ORSS71174.2026.11684282},
    url       = {https://ieeexplore.ieee.org/document/11684282}
}

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Non-Stationary Spectral Decomposition Network for Econometric Time Series Forecasting

Nikhil Sunder · The International FLAIRS Conference Proceedings, vol. 39, no. 1 · May 2026

NS-SDN represents an economic time series as a time-varying trend plus a sum of latent sinusoidal components whose amplitude, instantaneous frequency, and phase are emitted at each step from a recurrent latent state — an adaptive, state-driven spectral decomposition rather than a fixed-basis Fourier or stationary spectral model. The architecture draws on implicit neural representations with periodic activations, instantaneous-frequency analysis in the spirit of the Hilbert–Huang transform, and nonlinear state-space econometrics. It functions as both a conditional one-step forecaster and an interpretable spectral-analysis tool, exposing regime-dependent trend, amplitude, and frequency dynamics — demonstrated on the 10-Year Treasury yield through the 2020 macroeconomic shock.

Read on FLAIRS · PDF · Code

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Citation (BibTeX)

@article{Sunder_2026,
    title   = {Non-Stationary Spectral Decomposition Network for Econometric Time Series Forecasting},
    author  = {Sunder, Nikhil},
    journal = {The International FLAIRS Conference Proceedings},
    volume  = {39},
    number  = {1},
    year    = {2026},
    month   = {May},
    doi     = {10.32473/flairs.39.1.141588},
    url     = {https://journals.flvc.org/FLAIRS/article/view/141588}
}

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fedfred: A Python client for the Federal Reserve Economic Database (FRED) API

Nikhil Sunder · Zenodo · v3.0.0 · 2025

A citable software artifact providing full-coverage access to the Federal Reserve’s FRED, ALFRED, and GeoFRED/Maps endpoints through a typed Python interface, with sync and async clients, built-in rate limiting and caching, and native returns to pandas, polars, dask, and GeoPandas. Archived on Zenodo so that analyses depending on it can cite an exact, permanently resolvable version.

Zenodo record · Documentation · Code · Project detail →

Citation (BibTeX)

@software{fedfred,
  author    = {Sunder, Nikhil},
  title     = {fedfred: A Python client for the Federal Reserve Economic Database (FRED) API},
  version   = {3.0.0},
  year      = {2025},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.17635942},
  url       = {https://github.com/nikhilxsunder/fedfred}
}