Demand and forecasting with the Temporal Fusion Transformer
Hi everyone,
I hope you’re doing well!
- I’m new to both PyTorch and this forum, but I’ve been drawn to the Temporal Fusion Transformer (TFT) model because of its potential and usefulness.
- For the past two weeks, I’ve been trying to run the basic example from the tutorial here: Demand forecasting with the Temporal Fusion Transformer — pytorch-forecasting documentation.
- Unfortunately, despite my best efforts, I haven’t been able to get it to work.
- I’m using Windows, CUDA 12.4, Python, and Visual Studio Code.
- I’ve tried different combinations of Python versions (3.8, 3.10, 3.12) with Conda and Pip, and even Google Colab, but faced issues at various points.
- I’m starting to wonder if I’m missing something fundamental or if the example code has issues.
- Could someone kindly provide a simple, step-by-step guide for setting up the environment (specific versions of packages, installation commands, etc.)? Detailed instructions would be really helpful!
- I’ve encountered multiple errors, mostly package collisions and import issues, so I’ve been unable to get a clean run of the example.
- I’ve searched this forum and online resources, but while I’ve found fixes for individual issues, new ones keep arising with each step.
- I’m feeling stuck at this point, so any advice or suggestions would be greatly appreciated!
Thank you so much in advance for your help!
Regards from New York