Nikhil Gupta

[nixtla @nixtlainc]

🚀💙 Nixtla named as one of Fast Company’s Next Big Things in Tech 💙🚀

We're proud to share that we’ve been named to @FastCompany's fourth annual Next Big Things in Tech list, honoring emerging technology that has a profound impact for industries—from education and sustainability to robotics and artificial intelligence. We are honored to be chosen alongside other game-changing organizations for the #FCTechAwards. https://t.co/ppssL6uAj2

Time-series forecasting is a powerful method that leverages time-stamped data to predict future events and remove uncertainty from business conditions. Any organization that wants to perform forecasting or anomaly detection can benefit from a time series model. So, we aim to do for time series what LLMs have done for language - making them accessible to anyone. Our nixtlaverse open source ecosystem has more than 18,000 downloads and is used by forecasters around the world. Our foundation model TimeGPT has made it easier for anyone to forecast using data they already have in Excel or other systems – such as sales, revenue and inventory data - without having to hire a team of engineers to develop custom models. With TimeGPT, we make time-series forecasting available to anyone with just three lines of code.

“It’s a tremendous honor to have our time-series forecasting software recognized by Fast Company as groundbreaking technology that will have a profound impact on businesses,” says @MergenthalerMax, Co-Founder and CEO of Nixtla. “Nixtla’s users come from big companies and small, government agencies, nonprofits and academic institutions, and they can create insights and use data to make decisions in ways that were not possible before. Our team has worked hard to bring this technology to market and make time-series forecasting available to everyone.”

“The Next Big Things in Tech provides a fascinating glimpse at near- and long-term technological breakthroughs across a variety of sectors,” says Vaughan, editor-in-chief of Fast Company. “Spanning everything from semiconductors to agricultural gene editing, the companies featured in this year’s list are tackling some of the world’s most pressing and vexing problems.”

💙 We’re grateful for this honor and the amazing community who does the work every day that continues to make forecasting the next big thing.

⏰ Give forecasting a try or continue your time-series journey with our open source ecosystem and TimeGPT. https://t.co/kRpJyf1dS8

🚀 Happy forecasting!

#Forecasting #TimeSeries #MachineLearning #Python #DataScience

[nixtla @nixtlainc]

🚀 📈 Presenting our R package for working with TimeGPT 📈 🚀

We’re excited to present nixtlar v0.6.1, our R package for working with the TimeGPT API. As the R equivalent to nixtla, the Python SDK for TimeGPT, nixtlar was already available on CRAN and has now been updated to version 0.6.1. This new version incorporates the new TimeGPT API-v2 and extends support to tibbles.

TimeGPT is the first foundation model for time series forecasting. With TimeGPT you can quickly and easily generate accurate forecasts or conduct anomaly detection in just a few lines of code. nixtlar is designed to be user-friendly, allowing anyone within the R community to leverage generative AI for time series.

Capabilities of nixtlar include:
🏀 Zero-shot inference: Generate accurate forecasts with no prior model training.
💻 Fine-tuning: Enhance your predictions by fine-tuning the model to your specific dataset.
📊 Uncertainty quantification: Capture the full distribution of your predictions, either via quantile forecasts or prediction intervals.
🙌 R ecosystem integration: Works with your favorite R data structures—data frames, tibbles, or tsibbles.

nixtlar embodies Nixtla’s core philosophy to democratize access to state-of-the-art predictive insights, making advanced forecasting tools accessible to all.

🙏 Thank you for all the ideas and feedback from the R community that have contributed to nixtlar!

💙 Links in the thread for getting started with nixtlar.

And of course nixtlar already has its own hex sticker!

#Forecasting #TimeSeries #MachineLearning #AI #RStats #DataScience

[Nikhil Gupta @guptanick13]

I am excited to share that I have joined the team at @nixtlainc , where I will be working alongside Max Canseco, @azulgarza_, and other talented team members to contribute to their open-source projects and help advance the time series ecosystem in Python. Exciting times ahead!

[Aaron Levie @levie]

AI is evolving at a crazy pace. There’s a huge opportunity for a “GitHub Copilot” for almost all knowledge work (legal, finance, support, IT tickets, etc.), and the ability automate large sets of repetitive tasks to have humans freed up to focus on the harder ones.

[Nikhil Gupta @guptanick13]

While using the latest and greatest technique might sound fancy and be good for marketing, sometimes having a simpler, more explainable model which can be easily tweaked is far more valuable from a business perspective.

[Nikhil Gupta @guptanick13]

📈 The latest release of the #pycaret #timeseries module provides a convenient way to compare the performance of different models using the familiar plot_model interface.

📚 More here: https://t.co/GVu2bO6O54

#opensource #machinelearning #datascience #analytics #automl

[Nikhil Gupta @guptanick13]

⭐ Imagine you work in an industry where the lead time to procurement is long. The #pycaret #timeseries module supports multi-step forecasting horizons that are helpful in evaluating this type of problem statement.

🚀 More here: https://t.co/cNolchp5uq

#opensource #analytics

[Nikhil Gupta @guptanick13]

⭐ DYK: The #pycaret #timeseries module now comes integrated with #MLflow allowing you to manage the ML lifecycle, including experimentation & reproducibility!

🚀 More here: https://t.co/loEoyzOzY4

#opensource #machinelearning #datascience #forecasting #analytics #automl

[Nikhil Gupta @guptanick13]

⭐ Did you know: ⭐ The #pycaret #timeseries module now supports #prophet models (@Meta) You can also compare it against any of the ~ 30 other forecasting models in the library.

👉 More on this here: https://t.co/okniEWzame
🚀 All FAQs: https://t.co/V0Y1ST6UXI

#opensource

[Nikhil Gupta @guptanick13]

⭐ DYK: The #pycaret #timeseries module provides a unified platform to consistently compare models from #statsmodels, #pmdarima, #sklearn, #prophet, etc with a few lines of code.

👉 More here: https://t.co/EPAHjAqRRE
🚀 Docs: https://t.co/Za7c8Njczz

#opensource #sktime #automl

[Nikhil Gupta @guptanick13]

📈 How do you measure forecast stability? The Cycle-Over-Cycle Change (COCC) is a simple yet versatile metric that can be used to measure instability. Check out Stefan de Kok's blog for o9 Solutions, Inc.

📌 https://t.co/6vnbLkOXHw

#demandforecasting #stability #accuracy

[Nikhil Gupta @guptanick13]

🚀 Understanding the architecture of a library goes a long way in making sure we can use it to the best possible extent. This article aims to do this for the #pycaret #timeseries module with useful examples.

📌 https://t.co/RXqghghaZl

#opensource #machinelearning #datascience

[Nikhil Gupta @guptanick13]

📈 Forecasting is about more than just accuracy and value add. Stability and desirability are equally important for the business.

Check out Stefan de Kok's blog for o9 Solutions, Inc.

📌 https://t.co/DHA5PIqDyo

#demandforecasting #accuracy #stability #desirability

[Nikhil Gupta @guptanick13]

⭐ DYK: The #pycaret #timeseries module allows users to perform statistical analysis with ease using the "check_stats" method.

👉 More here: https://t.co/315xJ1Ut3x
🚀 All Docs: https://t.co/UTf67ukq93

#opensource #machinelearning #datascience #analytics #forecasting #automl

[Nikhil Gupta @guptanick13]

⭐ The #pycaret #timeseries module allows users to perform EDA and Model Fit assessments with ease using interactive #plotly plots.

👉 More here: https://t.co/wk5O4h4YjV
🚀 All Docs: https://t.co/vwrvUulqCK

#opensource #machinelearning #datascience #analytics #forecasting

[Nikhil Gupta @guptanick13]

💥 Myth Buster: DYK you can use low code libraries (like #pycaret) to get a better understanding of why not to use traditional methods to model stock price?

🚀 Learn more: https://t.co/mO5SBxs6Pa

#opensource #machinelearning #datascience #forecasting #timeseries #analytics

[Nikhil Gupta @guptanick13]

💥 Did You Know 💥 You can add your own @sktime_toolbox compatible models/pipelines to the #pycaret #timeseries module!

👉 More on this here: https://t.co/iwPHbnOkMo

#opensource #machinelearning #datascience #sktime #timeseries #forecasting #analytics #automl

[Nikhil Gupta @guptanick13]

💥 Myth Buster: DYK you can use low code libraries (like #pycaret) to get a better understanding of how models work and how they get built?

🚀 Explore the ARIMA Trend Term: https://t.co/Uv7Dx58cWR

#opensource #machinelearning #datascience #forecasting #timeseries #analytics

[Nikhil Gupta @guptanick13]

It is with mixed emotions that I share that today will be my last day at @TXInstruments. Thank you to everyone who was part of my wonderful journey in TI. While my tenure is coming to an end, I am also excited about what lies ahead. Will share update shortly.

#TIemployee

[Nikhil Gupta @guptanick13]

⭐ What does it take to convert a manual #timeseries workflow to a low code one with interactive visualizations & a consistent interface to popular modeling libraries?

💥 See for yourself: https://t.co/lywrVqGTKk

#opensource #machinelearning #datascience #pycaret @abhi1thakur