diff --git a/gists/timeseries/nhits/README.md b/gists/timeseries/nhits/README.md
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+# N-HiTS is a neural breakthrough — NNS needs no network 🚦
+
+[N-HiTS](https://arxiv.org/abs/2201.12886) (Neural Hierarchical Interpolation for
+Time Series, AAAI 2023) is one of the strongest deep-learning forecasters around.
+By combining **multi-rate input sampling** with **hierarchical interpolation** —
+built on the N-BEATS lineage — it reports roughly **20% better long-horizon
+accuracy than Transformer models at ~50× less compute**. It is, however, still a
+neural network: it needs a training loop, hyperparameters, and the hardware to go
+with them.
+
+Marco Peixeiro's walkthrough, [*All About
+N-HiTS*](https://www.datasciencewithmarco.com/blog/all-about-n-hits-the-latest-breakthrough-in-time-series-forecasting),
+applies N-HiTS to exactly this problem: forecast the last **120 steps** (five
+days) of the hourly **Interstate 94 Westbound** traffic-volume series, scored by
+MAE. This example runs the *same* dataset, the *same* `[:-120]` / `[-120:]`
+split, and the *same* metric — but with `NNS.ARMA` instead of a neural network:
+**no training loop, no hyperparameter search**, seasonal periods discovered
+directly from the data by `nns_seas`.
+
+## Result
+
+![NNS traffic forecast](nhits_traffic_forecast.png)
+
+```
+MAE on traffic data: 236.16791666666677
+```
+
+Head-to-head on the identical series, split, and metric (deep-learning numbers
+from the article):
+
+| Model | MAE | Beats the naive baseline? |
+|-------|----:|:--:|
+| **NNS.ARMA.optim** | **236** | ✅ |
+| Naive seasonal (weekly, K=168) | 249 | — |
+| N-HiTS | 266 | ❌ |
+| N-BEATS | worse than N-HiTS | ❌ |
+
+The article's punchline is that **neither N-HiTS nor N-BEATS beats the simple
+naive seasonal baseline** on this series ("the baseline still outperforms
+N-HiTS"). NNS is the only method here that does — an MAE of **236** versus the
+baseline's 249 and N-HiTS's 266 — and it gets there in seconds with none of the
+training machinery a neural model needs. (In fairness, the article notes this is
+a small, repetitive sample, which is partly why the baseline is so strong; the
+point is that NNS clears a bar the neural models did not.)
+
+## Run it
+
+```bash
+pip install ovvo-nns pandas numpy scikit-learn matplotlib
+python nhits_traffic.py
+```
+
+Writes `nhits_traffic_forecast.png` next to the script. Data is the hourly
+traffic-volume series from the
+[OVVO-Financial/NNS](https://github.com/OVVO-Financial/NNS) repo.
+
+## How it works
+
+```python
+seas_result = NNS.nns_seas(train_set, plot=False)
+periods = seas_result.get("periods", [])
+
+nns_estimates = NNS.nns_arma_optim(
+    variable=train_set,
+    h=120,
+    seasonal_factor=periods,
+    obj_fn=lambda predicted, actual: np.mean(np.abs(predicted - actual)),
+    objective="min",
+    negative_values=False,
+)
+```
+
+`nns_seas` finds the candidate seasonal periods; `nns_arma_optim` cross-validates
+combinations of them and forecasts `h=120` steps ahead. The `obj_fn` is set to
+MAE so the optimizer targets the same metric the benchmark reports. (`nns_arma_optim`
+silently discards any periods too long to estimate, so the full `nns_seas` list can
+be passed straight in.)
+
+---
+
+N-HiTS background from Marco Peixeiro's
+[*All About N-HiTS*](https://www.datasciencewithmarco.com/blog/all-about-n-hits-the-latest-breakthrough-in-time-series-forecasting)
+and the [original paper](https://arxiv.org/abs/2201.12886) (Challu, Olivares, et al.).
diff --git a/gists/timeseries/nhits/nhits_traffic.py b/gists/timeseries/nhits/nhits_traffic.py
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+"""N-HiTS is a neural breakthrough -- NNS forecasts the same traffic series with no network.
+
+N-HiTS (Neural Hierarchical Interpolation for Time Series, AAAI 2023) is a deep
+learning model -- multi-rate input sampling plus hierarchical interpolation, built
+on N-BEATS -- that reports ~20% better long-horizon accuracy than Transformer
+forecasters at a fraction of the compute. It still needs a GPU-era training loop.
+
+This script forecasts the last 120 steps of the hourly traffic-volume series with
+NNS.ARMA -- no neural network, no training loop, no hyperparameter search -- and
+lands a competitive MAE in a handful of lines. Seasonal periods are discovered
+directly from the data by nns_seas.
+
+See: https://www.datasciencewithmarco.com/blog/all-about-n-hits-the-latest-breakthrough-in-time-series-forecasting
+"""
+
+import matplotlib
+
+matplotlib.use("Agg")  # headless; drop this line for an interactive window
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error
+
+import nns as NNS
+
+# Load data
+url = (
+    "https://raw.githubusercontent.com/OVVO-Financial/NNS/"
+    "Data-and-Simulation-Routines/Datasets/traffic_data.csv"
+)
+df = pd.read_csv(url)
+
+# Columns are 'date_time' and 'traffic_volume'
+y = df["traffic_volume"].values
+
+# Train / test split -- hold out the last 120 points
+train_set = y[:-120]
+test_set = y[-120:]
+
+# Discover seasonal periods from the training data
+seas_result = NNS.nns_seas(train_set, plot=False)
+periods = seas_result.get("periods", [])
+
+# Optimize the seasonal ARMA combination (MAE objective to match the benchmark)
+nns_estimates = NNS.nns_arma_optim(
+    variable=train_set,
+    h=120,
+    seasonal_factor=periods,
+    obj_fn=lambda predicted, actual: np.mean(np.abs(predicted - actual)),
+    objective="min",
+    plot=False,
+    negative_values=False,
+)
+
+# Evaluate
+mae = mean_absolute_error(test_set, nns_estimates["results"])
+print(f"MAE on traffic data: {mae}")
+
+# Plot actual vs. forecast
+plt.figure(figsize=(10, 6))
+plt.plot(test_set, label="Actual Traffic", color="black", linewidth=2)
+plt.plot(nns_estimates["results"], label="NNS Forecast", color="blue", linewidth=2)
+plt.legend()
+plt.title("Traffic Volume Forecast (Actual Data)")
+plt.tight_layout()
+plt.savefig("nhits_traffic_forecast.png", dpi=120)  # so the README can show the figure
+plt.show()
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