Time Series Forecasting with AI Neural Networks (TabPFN Python Tutorial)

This tutorial shows you how to do time series forecasting in Python of cyclical data using TabPFN, a cutting-edge transformer-based neural network. You'll see how TabPFN's "zero-shot" capabilities, achieved through extensive pre-training on synthetic datasets, allow for accurate predictions without traditional model training or hyperparameter tuning.

The video shows you how to predict U.S. housing starts from the Federal Reserve Economic Data (FRED), demonstrating how TabPFN handles cyclical and seasonal patterns effectively. You'll learn how to quickly retrieve and preprocess economic data, generate point forecasts with built-in calibrated prediction intervals, and compare results to a straightforward XGBoost benchmark for context.

Importantly, this approach excels in modeling cyclical data, such as seasonal sales, employment rates, or other business or economic data series, though it isn't suitable for strongly trending series like stock prices without additional trend modeling. This tutorial is ideal for data scientists, machine learning engineers, and analysts interested in rapid, hardware-light forecasting solutions that can be easily adapted to other cyclical data.


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*Full Code*
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*Resources*
TabPFN: https://github.com/PriorLabs/TabPFN
TabPFN Time Series: https://github.com/PriorLabs/tabpfn-t...
FRED: https://fred.stlouisfed.org/

*Chapters*
0:00 What is TabPFN and Use Cases?
0:42 What this tutorial covers
1:15 Setup the TabPFN Python Environment
1:28 Library and Data imports
2:29 Data Preparation, Feature Engineering, Train/Test Split
3:16 How to run a TabPFN Neural Network model in Python
3:47 XGBoost Comparison
4:02 Calculating Mean Absolute Error and Plotting the Time Series
4:16 Results: Comparison of TabPFN to XGBoost
4:52 TabPFN Time Series Library