Loads and preprocesses data, trains a TFT model, and evaluates it with SMAPE.
Loads the production model and generates future forecasts, exporting predictions to CSV.
FloraCast is an end-to-end forecasting solution that demonstrates how to
build production-grade time series prediction workflows with ZenML and Darts.
It provides reproducible training, automatic model versioning, and scheduled
batch inference with rich visualizations to support demand and sales forecasting
use cases in retail, e-commerce, and supply chain.
Trains advanced forecasting models (Temporal Fusion Transformer by default; ExponentialSmoothing fallback)
Evaluates models with SMAPE and produces clear visual reports
Registers and promotes the best-performing models using ZenML's Model Control Plane
Runs automated batch inference to generate future forecasts on a schedule
Ingests ecommerce sales data and converts it into Darts TimeSeries objects
Applies standardized preprocessing, including train/validation split and frequency alignment
Trains a configurable TFT model and computes evaluation metrics
Stores artifacts with custom materializers, including timeseries visualizations
Loads the production model for batch inference and exports predictions to CSV
The architecture connects data ingestion, preprocessing, model training, evaluation,
and scheduled batch inference through ZenML pipelines, leveraging the Model Control
Plane for lineage, versioning, and promotion.
