#StatsForecast
My newsletter's first edition is out! 🥳

This week's main focus:
✅ Quarto's new features
✅ New learning resources
✅ Book of the week - Statistical Rethinking
✅ Introduction to the statsforecast library

www.linkedin.com/pulse/quarto...

#DataScience
Quarto New Features, Forecasting with Nixtla's statsforecast, and More
First, I wanted to thank you all for subscribing to the newsletter and for the support! This week's edition focuses on: Quarto's new features New learning resources Book of the week- Statistical Rethi...
www.linkedin.com
August 27, 2024 at 11:58 AM
Time Series Forecasting with MFLES
The newest method in StatsForecast
towardsdatascience.com
January 30, 2025 at 5:05 PM
Statistical vs Deep Learning forecasting methods

https://github.com/Nixtla/statsforecast/tree/main/experiments/m3

"a deep-learning ensemble that takes more than 14 days to run and costs around USD 11,000, outperforms a statistical ensemble that takes 6 minutes to run and costs $0.5c by … 1/3
github.com
February 19, 2024 at 3:18 PM
(1/4) Getting started with Nixtla's statsforecast library? Here is a short summary deck👇🏼

The statsforecast library, as the name implies, provides statistical forecasting models such as ARIMA, Holt-Winters, ETS, Theta, and many others 🎯.

#Python #DataScience #Stats #timeseries
September 14, 2024 at 3:15 PM
🤖 The AutoARIMA Arena: A no-holds-barred comparison of pmdarima, statsforecast, R's auto.arima, fable, and even Julia/Rust contenders! Who wins on speed, accuracy, and handling edge cases? (Spoiler: Python's catching up FAST!)
May 31, 2025 at 6:26 PM
🚀 @nixtlainc StatsForecast AutoETS Magic: Let model="ZZZ" Pick the Best Time Series Model for You!

Tired of guessing the right ETS model? AutoETS(model="ZZZ") does the heavy lifting:

✅ Automatically selects the best error, trend & seasonality combo (AAN, MAM, etc.)
July 8, 2025 at 4:00 PM
To use statsforecast, mlforecast, & neuralforecast, I've messed around with {reticulate} bindings in an experiment called {kantime} -- as I only bound their KAN model to {parsnip} then {modeltime}. Hopeful someone with more `time` 😏 could bind them all 🙌🏻
December 6, 2024 at 5:02 PM
autokitteh Durable workflow automation in just a few lines of code (9 mentions)

https://www.libhunt.com/r/autokitteh

Event Attributes
Autokitteh Alternatives and Reviews
Which is the best alternative to autokitteh? Based on common mentions it is: Statsforecast, Webvm, Starlark, Zerox, Multi-agent-orchestrator or Starlark-rust
www.libhunt.com
April 2, 2025 at 3:05 PM
April 26, 2025 at 6:06 AM
• Detecção de anomalias com estatística clássica antes de tentar AutoEncoders
• Uso de bibliotecas modernas como sktime, statsforecast e Darts
February 13, 2026 at 12:00 PM
(2/4) The statsforecast is easy to use and has great functionality. For example, ability to set object with multiple time series forecasting models and apply it on a multiple time series object.
September 14, 2024 at 3:18 PM
(2/2) This includes using different flavors of ARIMA methods from the statsmodels, pmdarima, skforecast, and statsForecast libraries. 

📖🔗: cienciadedatos.net/documentos/p...
ARIMA and SARIMAX models with python
Forecasting time series with arima and sarimax models using python and skforecast
cienciadedatos.net
April 3, 2024 at 1:45 PM
A new AI review! Nixtla/statsforecast ⭐4.2/5.0
statsforecast is a mature, production-oriented Python library for high-performance univariate time-series forecasting using a broad suite of statistical/econometric models (AutoARIMA/ETS/CES/Theta, MSTL, TBAT...
https://gitrated.com/Nixtla/statsforecast
June 11, 2026 at 7:17 PM
The whole algorithm fits in 15 lines of Python.

v0.1.2 adds:

- Orientation-correct finite-sample conformal quantile — closes the asymmetric L/R miss gap that affected v0.1.1
- CSPModel statsforecast-compatible wrapper now matches the core path to floating-point precision
June 13, 2026 at 6:28 PM
This isn’t about “new models.”

It’s about fixing a design that was never meant for modern workloads.

StatsForecast was a stepping stone.

Chronax is what happens when you actually rethink the execution model.
April 25, 2026 at 12:41 PM
For years, the Python time series ecosystem has been stuck in an awkward place.

StatsForecast was dubiously marketed as “fast” — but let’s be honest about what it actually is:

👉 a fairly awkward rewrite of Hyndman-style R libraries into Python
👉 with some acceleration layered on top
April 25, 2026 at 12:41 PM
⚡ Scales to thousands of series

Try "ZZZ"—your new forecasting shortcut! 🔥

'Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python' -> valeman.gumroad.com/...

#TimeSeries #Forecasting #Python #StatsForecast
Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python
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It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. 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This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.🎓 Want a Live, Interactive Learning Experience?Pair this book with the Modern Forecasting Mastery course on Maven.Join live cohort sessions with Valeriy, get direct feedback, and build models with peers.Next cohort → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $65) 🔥🔥🔥 Includes everything above, plus:✅ Premium Forecasting Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Forecast Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.If you find value or simply want to support the project, feel free to pay what it’s worth to you ❤️Ready to take your forecasting skills from stats to neural nets, and from theory to real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.
valeman.gumroad.com
July 8, 2025 at 4:01 PM
A special thanks to Mariana Menchero from the core Nixtla team, who joined us to share insights on the blazing-fast StatsForecast library. Her session sparked a fantastic discussion and Q&A — a true highlight of the program.

July 1, 2025 at 3:07 PM
A special thanks to Mariana Menchero from the core Nixtla team, who joined us to share insights on the blazing-fast StatsForecast library. Her session sparked a fantastic discussion and Q&A — a true highlight of the program.

June 29, 2025 at 5:41 PM
A special thanks to Mariana Menchero from the core Nixtla team, who joined us to share insights on the blazing-fast StatsForecast library. Her session sparked a fantastic discussion and Q&A — a true highlight of the program.

June 27, 2025 at 5:05 PM
This isn't just theory; it's packed with Python code examples (statsmodels, StatsForecast, pmdarima, skforecast, Darts), critical reflections (is Box-Cox overrated?), and the kind of insights that turn good forecasters into great ones.
May 31, 2025 at 6:26 PM
Spoiler: The book proves it.
ARIMA's surprising connection to Vapnik's Statistical Learning Theory (VC dimension enters the chat!).
👨‍💻 Code That Cuts the Fluff: No filler.
Discover the best forecasting libraries:
StatsForecast (Nixtla) | statsmodels | pmdarima | ETNA | R | Julia
May 31, 2025 at 5:18 PM