Advanced Time-Series Forecasting Models (e.g., Prophet/DeepAR)

Amazon's DeepAR uses deep learning and recurrent neural networks, making it ideal for training across multiple related time-series simultaneously. Facebook's Prophet, on the other hand, uses an additive model and is generally easier to use out-of-the-box for single time-series with strong seasonal effects. DeepAR requires more data and tuning but can significantly outperform Prophet on complex, large-scale datasets.

Yes, Prophet has built-in functionality to incorporate country-specific holidays directly into its forecasting model. You can simply pass a custom dataframe of holidays to the model, and it will create regressors to account for their impact on the data. This makes it highly effective for retail sales forecasting where holidays cause significant traffic and purchasing spikes.

Because DeepAR is a deep learning model based on recurrent neural networks, using a GPU is highly recommended for efficient training. While you can technically train it on a CPU for very small datasets, it will be significantly slower. Utilizing cloud instances like AWS SageMaker or a local Nvidia RTX series GPU will drastically reduce training time.

Many modern deep learning models like DeepAR can gracefully handle missing data points by treating them as latent variables during the training process. Prophet automatically handles missing values and irregularly spaced data without requiring explicit imputation. However, for best results, you should still clean large gaps in your time-series data before feeding it to the algorithms.