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From ARIMA to TimeGPT — A New Era in Time Series Prediction (Part II)
- Fine-tuning is crucial for improving the performance of models like TimeGPT.
- The fine-tuning process led to a 50% reduction in error.
- TimeGPT outperformed classical methods like Exponential Smoothing and Prophet.
- TimeGPT's fine-tuning capabilities provide a more efficient and scalable solution for time series forecasting.
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