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Add event context to your forecasts with TimeGPT categorical variables ⚡ Categorical variables capture discrete event types (promotions, holidays, seasons) that drive demand patterns in time series data. Without them, models treat all time periods uniformly, missing the signal from events that influence demand patterns. TimeGPT handles categorical variables with a single parameter: pass your encoded events as X_df and the model incorporates them automatically. See the first comment for the complete guide 📖 #TimeSeries #Forecasting #AI #TimeGPT

Think foundational models are only for text? Check TimeGPT-2, Nixtla forecasting model! Nixtla just announced 𝗧𝗶𝗺𝗲𝗚𝗣𝗧-𝟮 — the next generation of their foundation models for forecasting. 🤔 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄? → 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗳𝗮𝗺𝗶𝗹𝘆 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹𝘀 Choose Mini, Standard, or Pro depending on your compute vs. accuracy tradeoffs. → 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Up to 𝟲𝟬% 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 vs. the previous generation, ranked top-3 on benchmarks like GiftEval, FEV, and VN1. → 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗿𝗲𝗮𝗱𝘆  On-premise deployment in minutes, scales to millions of series, SOC-2 compliant. ✅ 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:  Forecasting has always been fragmented — custom models per use case, painful tradeoffs between latency, accuracy, and deployment constraints. TimeGPT-2 is a serious step toward 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that can actually run across industries (retail, logistics, finance, energy, IoT). Takeaway: If you’re still stitching together Prophet + XGBoost + LSTMs for production demand forecasting? Try to benchmark against TimeGPT-2! 👉 𝗝𝗼𝗶𝗻 𝗳𝗼𝗿 𝗲𝗮𝗿𝗹𝘆 𝗮𝗰𝗰𝗲𝘀𝘀 𝗵𝗲𝗿𝗲: https://lnkd.in/e5c7BWQr

Validate time series models the right way with cross-validation ⚡ Cross-validation tests your model on multiple time windows to ensure it generalizes well to future data. However, setting up temporal splits and running validation loops is slow and complex. StatsForecast makes this simple with one method call. Just specify: • Forecast horizon (h) - how far ahead to predict • Step size - interval between validation windows • Number of windows (n_windows) - how many times to validate StatsForecast handles the rest with distributed operations across your CPU cores, making validation significantly faster. See the first comment for the complete guide 📖 #TimeSeries #Forecasting #DataScience #MachineLearning

🎉 We had a great time joining Nicolas Vandeput and Tyler Blume for this webinar on the VN2 Competition! ⭐ Good luck to all participants who will be submitting their first orders next week. We are excited to see what this competition will reveal about forecasting and inventory planning. 👇 You can now watch the full recording on Nicolas’s YouTube channel and explore our notebooks in the DataSource.ai platform to help you get started with the forecasting part of the competition. #forecasting #timeseries #VN2 #Nixtla

Improve forecast accuracy with TimeGPT's exogenous variables 📊 Including external variables like weather, marketing spend, or holidays helps capture the real drivers behind your forecasts, reducing prediction errors. This approach lets you leverage domain knowledge and account for planned events, moving beyond pure historical pattern recognition. TimeGPT incorporates numeric exogenous variables alongside historical data to capture external variables. The implementation is straightforward: • Include exogenous variables in your historical data (df) • Create future exogenous values with the same columns • Pass future values via X_df parameter • TimeGPT learns relationships between external factors and your target variable The plot below shows the difference: basic forecasts follow smooth trends, while exogenous-enhanced forecasts capture day-of-week patterns and external factor volatility. See the first comment for the complete guide 📖 #TimeSeries #Forecasting #DataScience #TimeGPT

🚀 Introducing the TimeGPT-2 family: next-generation time-series foundation models Today, we’re announcing the private preview of TimeGPT-2 Mini, TimeGPT-2, and TimeGPT-2 Pro, built for reliable, enterprise-grade time series forecasting. The TimeGPT-2 family is optimized for enterprise needs, prioritizing accuracy and stability with a privacy-first approach and full support for self-hosted and on-premises deployments. After extensive testing, the new family of models shows up to 60% accuracy improvement for enterprise use cases compared to the previous generation. We also ran exhaustive benchmarking on public baselines: consistently ranks in the top 3 on benchmarks such as GiftEval, FEV, and VN1 (reproducible results available upon request). TimeGPT-2 marks a new milestone in time-series modeling and is already delivering real value for Fortune 1000 companies in retail, logistics, finance, energy, and IoT. This is the first of three releases rolling out in the coming weeks. Stay tuned. 📩 We’ve opened pilot programs for select organizations. Sign up here for early access: https://bit.ly/4oold5w #TimeGPT2 #timeseries #forecasting #AI #MLOps #analytics #supplychain #energy #finance #IoT

Handle noisy data and outliers with Huber loss 📈 Standard loss functions are sensitive to outliers, causing models to overfit to anomalies and produce unstable forecasts. Huber loss in NeuralForecast provides robust training that's less sensitive to outliers while maintaining accuracy. In the plot below, normal distribution loss overfits to every anomaly in the forecast period, while Huber Loss maintains consistent predictions despite noisy training data. See the first comment for the complete guide 📖 #DataScience #TimeSeries #MachineLearning #Python

Build an adaptive monitoring system with rolling forecasts 🎯 Production metrics drift with growth and seasonality, which makes static alerts unreliable. For example, if you set an alert at 5k when your baseline later moves to 10k, you will be flooded with useless alerts, and you will start ignoring the system. In our latest article, you will learn how to build an adaptive monitoring system with rolling forecasts using TimeGPT-1. The process is simple: 1. Train the model on all daily spend up to today 2. Forecast tomorrow with a 99 percent confidence band 3. When tomorrow arrives, alert only if the actual falls outside the band by a meaningful amount 4. Add the new data point to the training set and repeat See the first comment for the complete guide 📖 #DataScience #MachineLearning #TimeSeries #AnomalyDetection

Tomorrow’s the day. Join our VN2 inventory planning webinar to learn actionable forecasting workflows and get your questions answered in a live Q&A. Continue the discussion in Slack. 🚀 Not registered yet? Register here: https://bit.ly/3IDbVDp ☕️ Join the conversation on Slack: https://bit.ly/3WilwT4

The community asked, and Nixtla responded! ✅ We’re excited to announce a time series webinar to help you get started in the VN2 inventory planning competition created by Nicolas Vandeput. In VN1, participants like Justin Furlotte, Arsa Nikzad, Antoine Schwartz, and An Hoang used Nixtla libraries to build winning solutions, and now we’ll show you how to do the same. In this live webinar, Nixtla's experts will share concrete tips and code examples on how to easily build forecasting solutions. Participants are welcome to interact and ask questions at the end. We have also created a Slack channel where you can suggest topics you’d like covered and/or ask any questions. 🔗 View all relevant links in the comment.