Optimize PyTorch Time-Series Model
Budget: €30 – €250 EUR
I already have a working PyTorch model that forecasts petrol-price time-series data, yet its accuracy is not where it needs to be. The core goal is to push prediction performance higher without bloating runtime or resource usage.
You will receive:
• the current repo (Python 3, PyTorch 2.x)
I would like you to:
• craft smarter feature engineering steps (calendar effects, rolling stats, lags, etc.)
• tune hyperparameters systematically (learning rate, hidden sizes, sequence length, regularisation)
• rethink or refine the model architecture if it unlocks accuracy gains (e.g., temporal CNN layers, attention, Transformer-style blocks)
• keep RAM and CPU footprints reasonable so the model can still train on a mid-range workstation
Please document every change clearly inside the code and in a short README so I understand both the rationale and how to reproduce your results.
You will receive:
• the current repo (Python 3, PyTorch 2.x)
I would like you to:
• craft smarter feature engineering steps (calendar effects, rolling stats, lags, etc.)
• tune hyperparameters systematically (learning rate, hidden sizes, sequence length, regularisation)
• rethink or refine the model architecture if it unlocks accuracy gains (e.g., temporal CNN layers, attention, Transformer-style blocks)
• keep RAM and CPU footprints reasonable so the model can still train on a mid-range workstation
Please document every change clearly inside the code and in a short README so I understand both the rationale and how to reproduce your results.