PROGRAM | Data Platforms | 2021 -> 2022
Automated Time-Series Forecasting Framework
Benchmark -> train -> evaluate -> iterate, with repeatable objectives.
PythonTime-seriesModel benchmarkingReproducible pipelines
- Semi-automatic end-to-end forecasting workflow with consistent evaluation.
- Model benchmarking across candidate families under realistic constraints.
- Designed to be rerun safely (same inputs -> comparable outputs).
Context
- Forecasting is a process: data quality, objectives, and evaluation discipline.
- Stakeholders care about error under specific regimes (seasonality, shocks, sparsity).
What we built
- A repeatable pipeline: dataset prep -> baselines -> candidate training -> evaluation -> reporting.
- Benchmark harness to compare models under the same splits and metrics.
- Testing discipline that reduces regression risk when models or features change.
Outcome
- Faster iteration cycles, fewer one-off notebooks, easier handover and maintenance.
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