---
name: nixtla-28e23188ab53
description: "Nixtla ecosystem — statsforecast (statistical), neuralforecast (deep learning), hierarchicalforecast, and MLForecast. Production time series forecasting with AutoARIMA, ETS, Theta, Transformers, and ensemble blending."
tags: [nixtla, time-series, forecasting, arima, deep-learning, hierarchical, zorai]
---
## Overview

Nixtla provides time series forecasting with multiple backends — StatsForecast (statistical), NeuralForecast (deep learning), and HierarchicalForecast (hierarchical reconciliation). Covers ARIMA, ETS, Prophet, Theta, N-BEATS, DeepAR, Temporal Fusion Transformer, and more.

## Installation

```bash
uv pip install nixtla
```

## Statistical Forecasting (StatsForecast)

```python
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, ETS, Theta

models = [AutoARIMA(season_length=12), ETS(season_length=12), Theta(season_length=12)]
sf = StatsForecast(models=models, freq="M")

# df needs ds (date), y (value), unique_id columns
forecasts = sf.forecast(df, h=12)
print(forecasts)
```

## Deep Learning (NeuralForecast)

```python
from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS, NHITS

nf = NeuralForecast(models=[NBEATS(input_size=24, h=12), NHITS(input_size=24, h=12)])
nf.fit(df)
forecasts = nf.predict()
```

## Hierarchical Reconciliation

```python
from hierarchicalforecast import HierarchicalForecast
from hierarchicalforecast.methods import BottomUp, TopDown

hf = HierarchicalForecast(models=forecasts, reconcilers=[BottomUp(), TopDown()])
hf.reconcile(S_hierarchy)
```

## References
- [Nixtla docs](https://nixtla.github.io/nixtla/)
- [Nixtla GitHub](https://github.com/Nixtla/nixtla)

---
Source attribution: mkurman
Original project: https://github.com/mkurman/zorai/blob/d06ee7597049567dab92db22d41b2764cf89094a/skills/scientific-skills/nixtla/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 zorai contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: mkurman
Original project: https://github.com/mkurman/zorai/blob/d06ee7597049567dab92db22d41b2764cf89094a/skills/scientific-skills/nixtla/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Marketing, Sales
Roles: Business Development, Data Analyst, Data Scientist, Growth Marketer, ML / AI Engineer
Specialties: API, Backend
Tools: Nixtla
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 913357cc28186b3a9408904c33537a3947e388d6815979e14484603ae376b3a8