Automated Time-Series Forecasting Framework

01 / Evidence

Outcome

• Faster iteration cycles.
• Fewer one-off notebooks.
• Easier handover and maintenance.

02 / Operating environment

Context

Benchmark -> train -> evaluate -> iterate, with repeatable objectives.

Teams operating repeatable forecasting workflows

03 / Boundary conditions

Constraints

1. C01

   Data quality, objectives, and evaluation discipline are part of the forecasting process.

2. C02

   Error must be measured under seasonality, shocks, and sparsity.

3. C03

   The workflow must be safe to rerun with comparable outputs.

04 / Decision path

Architecture

05 / Engineering choices

Key decisions

• D1Standardize dataset preparation, baselines, candidate training, evaluation, and reporting.
• D2Compare candidate families under the same splits and metrics.
• D3Use testing discipline to reduce regression risk when models or features change.

06 / Proof discipline

Evaluation

• Benchmark candidate model families under consistent objectives.
• Compare runs using the same inputs, splits, and metrics.
• Evaluate error under realistic operating regimes.

07 / Scope

Ownership

Forecasting pipeline, benchmark harness, and evaluation framework engineering

1. O01

   Dataset preparation and baseline workflow

2. O02

   Candidate training and benchmark harness

3. O03

   Evaluation, reporting, and regression discipline

08 / Explicit compromises

Trade-offs

• Prefer repeatable objectives and comparable runs over one-off model tuning.
• Treat realistic regimes as evaluation constraints rather than optimizing one aggregate error.