Skip to main content
Ctrl+K
ns-sdn - Home ns-sdn - Home
  • Getting started
  • User guide
  • Examples
  • API reference
  • Engine reference
  • Project
  • GitHub
  • PyPI
  • Codecov
  • Getting started
  • User guide
  • Examples
  • API reference
  • Engine reference
  • Project
  • GitHub
  • PyPI
  • Codecov

Section Navigation

  • ns_sdn.spectral_decomposition
    • NSSDN
      • NSSDN.alignment
      • NSSDN.device
      • NSSDN.endog
      • NSSDN.exog
      • NSSDN.exog_dim
      • NSSDN.gating
      • NSSDN.k
      • NSSDN.nobs_total
      • NSSDN.objective
      • NSSDN.ordered_frequency
      • NSSDN.penalties
      • NSSDN.positive_frequency
      • NSSDN.scaling
      • NSSDN.schedule
      • NSSDN.seed
      • NSSDN.state_dim
      • NSSDN.time
      • NSSDN.transition
      • NSSDN.fit
    • NSSDNResult
      • NSSDNResult.endog
      • NSSDNResult.time
      • NSSDNResult.exog
      • NSSDNResult.k
      • NSSDNResult.state_dim
      • NSSDNResult.alignment
      • NSSDNResult.transition
      • NSSDNResult.ordered_frequency
      • NSSDNResult.gating
      • NSSDNResult.objective
      • NSSDNResult.scaling
      • NSSDNResult.scale
      • NSSDNResult.emission
      • NSSDNResult.fittedvalues
      • NSSDNResult.resid
      • NSSDNResult.llf
      • NSSDNResult.nobs
      • NSSDNResult.n_params
      • NSSDNResult.sigma2
      • NSSDNResult.trend
      • NSSDNResult.amplitude_raw
      • NSSDNResult.amplitude
      • NSSDNResult.frequency
      • NSSDNResult.phase
      • NSSDNResult.phase_offset
      • NSSDNResult.gate
      • NSSDNResult.state
      • NSSDNResult.mix
      • NSSDNResult.history
      • NSSDNResult.predictor
      • NSSDNResult.aic
      • NSSDNResult.bic
      • NSSDNResult.components
      • NSSDNResult.effective_amplitude
      • NSSDNResult.hqic
      • NSSDNResult.information_criteria
      • NSSDNResult.observed
      • NSSDNResult.oscillation
      • NSSDNResult.period
      • NSSDNResult.power_share
      • NSSDNResult.r_squared
      • NSSDNResult.reconstruction_resid
      • NSSDNResult.compare
      • NSSDNResult.decompose
      • NSSDNResult.forecast
      • NSSDNResult.predict
      • NSSDNResult.spectral_state
      • NSSDNResult.summary
      • NSSDNResult.to_dict
      • NSSDNResult.to_pandas
    • Penalties
      • Penalties.amplitude
      • Penalties.frequency
      • Penalties.phase_curvature
      • Penalties.trend
      • Penalties.sparsity
      • Penalties.spectrum
    • Stage
      • Stage.learning_rate
      • Stage.steps
      • Stage.penalties
  • ns_sdn.spectral_state
    • SpectralState
      • SpectralState.time
      • SpectralState.frequency
      • SpectralState.weight
      • SpectralState.centroid
      • SpectralState.dispersion
      • SpectralState.entropy
      • SpectralState.effective_count
      • SpectralState.participation_ratio
      • SpectralState.velocity
      • SpectralState.velocity_frequency
      • SpectralState.velocity_mass
      • SpectralState.active
      • SpectralState.contribution_correlation
      • SpectralState.disentanglement_score
      • SpectralState.active_threshold
      • SpectralState.k
      • SpectralState.nobs
      • SpectralState.from_result
      • SpectralState.summary
      • SpectralState.to_dict
      • SpectralState.to_pandas
  • ns_sdn.summary
    • InformationCriteria
      • InformationCriteria.aic
      • InformationCriteria.bic
      • InformationCriteria.hqic
      • InformationCriteria.count
      • InformationCriteria.from_likelihood
      • InformationCriteria.index
    • SummaryTable
      • SummaryTable.title
      • SummaryTable.metadata
      • SummaryTable.columns
      • SummaryTable.rows
      • SummaryTable.notes
      • SummaryTable.to_pandas
      • SummaryTable.to_text
  • ns_sdn.typing
    • Alignment
    • Objective
    • Scaling
    • Transition
  • ns_sdn.exceptions
    • DimensionError
    • NSSDNError
    • NumericalError
    • SpecificationError
  • API reference
  • ns_sdn.spectral_decomposition
  • NSSDNResult
  • NSSDNResult.trend

NSSDNResult.trend#

NSSDNResult.trend: NDArray[float64]#

previous

NSSDNResult.sigma2

next

NSSDNResult.amplitude_raw

On this page
  • NSSDNResult.trend

User guide

The model in its equations, how to read the decomposition it emits, and the spectral-state coordinates that summarize it.

Read the guide ›

Examples

A synthetic chirp with a known answer, then the 10-year Treasury yield from FRED, as executed scripts with their figures.

View the examples ›

API reference

The specification, the result and the spectral state, with every path they carry and a runnable example on each.

View the API ›

Get ns-sdn

Releases are published to PyPI and announced on GitHub Releases. Pin the exact version in anything that must reproduce.

pip install ns-sdn==1.0.0

Resources

  • Releases
  • Changelog
  • Issues
  • Citing
  • License

Related

  • cultivars time series econometrics
  • fedfred FRED client
  • edgar-sec SEC EDGAR client
  • GitHub
  • PyPI
  • Codecov
  • Cite

© 2026, Nikhil Sunder. Released under the MIT License. ns-sdn is an independent open-source project and is not affiliated with the Federal Reserve Bank of St. Louis; FRED® is a registered trademark of the Federal Reserve Bank of St. Louis.

ns-sdn 1.0.0 · built with Sphinx 9.1.0 and the PyData Sphinx Theme