aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-PyTorch
This repository compares the performance of 8 different regression loss functions used in Time Series Forecasting using Temporal Fusion Transformers.
This project helps data scientists and machine learning engineers fine-tune time series forecasting models. By comparing how different loss functions perform with Temporal Fusion Transformers, it shows which methods yield the most accurate predictions for future volumes. The user provides historical time series data, and the project outputs evaluated loss function metrics, helping them choose the best approach for their specific forecasting challenge.
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Use this if you are a data scientist or ML engineer looking to improve the accuracy of your multi-horizon time series forecasts by systematically evaluating different regression loss functions with state-of-the-art models like Temporal Fusion Transformers.
Not ideal if you are a business user or analyst needing a ready-to-use forecasting tool without deep technical knowledge of machine learning models and loss functions.
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Feb 21, 2025
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