OVVO-Financial · OVVO-Financial · Jun 14, 2026 · Jun 14, 2026
diff --git a/README.md b/README.md
@@ -122,7 +122,6 @@ print("forecast:", forecast)
 | Causation | `nns_causation`, `causal_matrix` |
 | Regression and classification | `nns_reg`, `nns_m_reg`, `nns_stack`, `nns_boost` |
 | Forecasting | `nns_seas`, `nns_arma`, `nns_arma_optim`, `nns_var` |
-| Nowcast panels | `nns_nowcast_panel`, `CsvNowcastProvider` |
 | Distribution tools | `nns_cdf`, `nns_anova`, `nns_norm` |
 | Stochastic dominance | `fsd`, `ssd`, `tsd`, `nns_sd_cluster`, `sd_efficient_set` |
 | Stochastic superiority and simulation | `nns_ss`, `nns_mc`, `nns_meboot` |
@@ -157,7 +156,6 @@ Runnable examples live in [`docs/examples`](docs/examples):
 | Regression | [`regression.py`](docs/examples/regression.py) |
 | Classification | [`classification.py`](docs/examples/classification.py) |
 | Forecasting | [`forecasting.py`](docs/examples/forecasting.py) |
-| Nowcast panel | [`nowcast_panel.py`](docs/examples/nowcast_panel.py) |
 
 Run one example:
 
@@ -179,7 +177,6 @@ Notebook workflows are also available under [`docs/examples/notebooks`](docs/exa
 - [Behavior conventions and intentional divergences](docs/conventions.md)
 - [Benchmarks](docs/benchmarks.md)
 - [Examples](docs/examples/README.md)
-- [Nowcast design](docs/specs_nowcast.md)
 
 ## Development
 

diff --git a/docs/api_status.md b/docs/api_status.md
@@ -54,8 +54,6 @@ invariant, and property coverage.
 | Boost: `nns_boost` | partial | medium | Deterministic and stochastic structures are implemented; one high-feature threshold path remains guarded to match installed-R failure behavior. |
 | Seasonality: `nns_seas` | implemented | high | Non-plotting installed-R path is implemented and cached defensively. |
 | ARMA and VAR: `nns_arma`, `nns_arma_optim`, `nns_var` | partial | medium | Numeric forecasting and supported VAR dimension-reduction paths are implemented on focused fixtures. Explicit numeric multi-lag ARMA uses actual-lag weighting instead of installed R's position-based weighting quirk. VAR's multivariate stack stage matches R's effective time-series holdout sizing; the remaining macro-like VAR strict xfail is inherited from ARMA optimizer period selection. Stochastic interval streams are structural/statistical parity only. |
-| Nowcast panel: `nns_nowcast_panel` | implemented | medium | Python-native deterministic monthly panel helper backed by `nns_var`. R NNS 13.0 does not export `NNS.nowcast`, so this is no longer an R-export parity target. |
-| Providers: `CsvNowcastProvider` | implemented | medium | Produces explicit local/offline payloads for `nns_nowcast_panel`. |
 | Bootstrap/Monte Carlo: `nns_meboot`, `nns_mc` | implemented | medium | Deterministic diagnostics are parity-tested; exact stochastic replicate parity with R is not expected. |
 | Stochastic dominance/superiority: `fsd`, `ssd`, `tsd`, `.uni` wrappers, `nns_ss`, `nns_sd_cluster`, `sd_efficient_set` | implemented | medium | Public structures and deterministic paths are covered. SD uses exact pure-NumPy prefix-pair kernels plus a degree-1 discrete order-statistic matrix path; R's C++ core remains faster on full finance fixtures. Stochastic intervals use NNS Python RNG. |
 | ANOVA: `nns_anova` | implemented | high | Binary, multi-group, pairwise, and degenerate `NaN` conventions are covered. |
@@ -75,10 +73,6 @@ invariant, and property coverage.
 ## Intentional Design Boundaries
 
 - No hidden network fetching happens by default.
-- NNS Python does not export `nns_nowcast`; R NNS 13.0 does not export `NNS.nowcast`.
-- Nowcast providers are payload builders for `nns_nowcast_panel`, not implicit
-  public forecast wrappers.
-- `CsvNowcastProvider` is local/offline.
 - Library code does not auto-load `.env` files.
 - External data clients and dataframe libraries are not dependencies.
 - NNS Python uses explicit Python errors for some cases where R silently truncates,
@@ -94,23 +88,6 @@ invariant, and property coverage.
   degree-1 discrete calls. Optional compiled SD backends remain deferred until
   benchmark evidence justifies the added packaging and maintenance cost.
 
-## Provider Boundary
-
-Nowcast provider support is explicit. Providers return payloads; callers pass
-the payload to `nns_nowcast_panel`:
-
-```python
-from nns import nns_nowcast_panel
-from nns.providers import CsvNowcastProvider
-
-provider = CsvNowcastProvider("monthly_panel.csv")
-payload = provider.fetch((), "2000-01-03")
-result = nns_nowcast_panel(payload["series"], h=2, tau=12, dates=payload["dates"])
-```
-
-NNS Python does not ship a default Yahoo, FRED, or other live-data workflow hidden
-behind a public nowcast wrapper.
-
 ## Intentional Divergences And Caveats
 
 The detailed behavior notes live in `docs/conventions.md`. Release-relevant

diff --git a/docs/conventions.md b/docs/conventions.md
@@ -404,20 +404,6 @@ logic, and R-style relevance extraction. The function returns `multivariate`
 and `relevant_variables` in the same shape/naming pattern expected by
 `NNS.VAR`.
 
-`nns_nowcast_panel` is the deterministic nowcast core for user-supplied monthly
-numeric panels. It accepts array-like panels or ordered mappings of column names
-to numeric series, delegates numeric forecasting to `nns_var`, and returns VAR
-fields plus `dates` and `metadata` dictionaries. Date labels are metadata rather
-than array indices. Without dates, forecast rows are labeled `t+1`, `t+2`, ...
-With dates, inputs are normalized to `YYYY-MM`, must be sorted and unique, and
-forecast labels advance monthly. R NNS 13.0 does not export `NNS.nowcast`, so NNS Python
-does not export a public `nns_nowcast` wrapper. `CsvNowcastProvider` remains an
-explicit payload builder whose `fetch(series, start_date)` method returns
-`{"series": ..., "dates": ..., "metadata": ...}` for callers to pass to
-`nns_nowcast_panel`. `CsvNowcastProvider` is offline and local-file only.
-Library code does not read `.env` files. NNS Python does not ship an implicit
-FRED/Yahoo provider.
-
 ## Meboot
 
 `nns_meboot` maps to R's `NNS.meboot` maximum-entropy bootstrap algorithm and

diff --git a/docs/examples/README.md b/docs/examples/README.md
@@ -25,15 +25,13 @@ you want short Python call patterns that are kept in sync with NNS Python.
 | Regression | [regression.py](regression.py) | `nns_reg`, fitted values, point estimates, regression output shape | `NNSvignette_Clustering_and_Regression.Rmd` |
 | Classification | [classification.py](classification.py) | `nns_reg(..., type="class")`, numeric class-code predictions | `NNSvignette_Classification.Rmd` |
 | Forecasting | [forecasting.py](forecasting.py) | `nns_arma`, `nns_arma_optim`, `nns_var` | `NNSvignette_Forecasting.Rmd` |
-| Nowcast panel | [nowcast_panel.py](nowcast_panel.py) | deterministic user-supplied panel, date metadata, VAR-backed forecast output | `NNS.VAR` nowcast/frequency-alignment material |
 
 ## Notebooks
 
 | Topic | Notebook |
 |---|---|
 | Partial-moment risk workflow | [01_partial_moments_risk_workflow.ipynb](notebooks/01_partial_moments_risk_workflow.ipynb) |
 | Regression, classification, factors | [02_regression_classification_workflow.ipynb](notebooks/02_regression_classification_workflow.ipynb) |
-| Forecasting and local nowcast panel | [03_forecasting_nowcast_workflow.ipynb](notebooks/03_forecasting_nowcast_workflow.ipynb) |
 | Distribution, dominance, simulation | [04_distribution_dominance_simulation_workflow.ipynb](notebooks/04_distribution_dominance_simulation_workflow.ipynb) |
 | Boston Housing regression parity example | [05_boston_housing_regression_workflow.ipynb](notebooks/05_boston_housing_regression_workflow.ipynb) |
 

diff --git a/docs/examples/notebooks/03_forecasting_nowcast_workflow.ipynb b/docs/examples/notebooks/03_forecasting_nowcast_workflow.ipynb