Spike: Consider Using Darts for Managing Prophet and Other Forecasting Models

[dominiquekleeven]

Investigate the feasibility and benefits of using Darts to manage forecasting model providers.

Darts is a framework for time series forecasting that supports multiple models (including Prophet, XGBoost, LightGBM, etc.) with a consistent API. It also includes built-in support for regressors and common time series preprocessing, which may reduce the need for custom feature engineering.

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Goals

• Evaluate how Darts can simplify model management
• Compare current Prophet implementation with a Darts-based equivalent
• Explore support for future model types via Darts (e.g. XGBoost, RNNs)

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Notes on Pre-processing

Before Darts can be used, data pre-processing needs to be properly set up:

• Darts expects all time series data (target and covariates) to be resampled to a fixed interval
• Missing data must be handled explicitly during pre-processing
• Current Prophet implementation handles much of this internally, but Darts shifts this responsibility to the service

While this isn't a large amount of work, it's important to account for during migration.

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Benefits

• Easy addition of new forecasting ML/stat models
• Native support for time series preprocessing and regressors
• Built-in backtesting and evaluation/validation utilities
• Potential reduction in boilerplate and custom logic

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