Favorita: Time-Series Models (ARIMA/SARIMA)
Classical time-series forecasting — ARIMA and SARIMA — train, tune, and predict, with shared time-series utilities. The statistically grounded counterpoint to the tree-based models, and often the right tool for clean seasonal series.
Primary Outcome
Trained classical time-series forecasts that capture trend and seasonality, comparable against the ML models.
Solution
ARIMA/SARIMA train, tune, and predict scripts with shared utilities, run alongside the ML models for a fair comparison on the same data.
Deliverables
train_timeseries.py— fit ARIMA/SARIMA modelsoptimize_timeseries.py— order and seasonal-order searchpredict_timeseries.py— forecast generationts_common.py— shared time-series helpers
Strategic Context
Classical time-series methods are unfashionable and frequently correct. For clean, seasonal demand, a SARIMA model is interpretable, cheap, and competitive. Keeping it in the lineup guards against the reflex to reach for complexity the data doesn't require.
Technical Architecture
Classical time-series methods are unfashionable and frequently correct. For clean, seasonal demand, a SARIMA model is interpretable, cheap, and competitive. Keeping it in the lineup guards against the reflex to reach for complexity the data doesn't require.
Problem Statement
Teams default to ML models even for clean seasonal series where a classical method would be more interpretable and just as accurate.
Links
What's Included
The vertex/models/timeseries directory: train_timeseries.py, optimize_timeseries.py, predict_timeseries.py, and ts_common.py for shared seasonality and differencing helpers.
FAQs
When do classical methods win?
On clean, strongly seasonal series with limited exogenous drivers, a well-specified SARIMA is often interpretable and competitive.
Is Prophet included?
This template focuses on ARIMA/SARIMA via statsmodels; the structure makes adding Prophet straightforward.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4
Primary Outcome
Trained classical time-series forecasts that capture trend and seasonality, comparable against the ML models.
Problem Statement
Teams default to ML models even for clean seasonal series where a classical method would be more interpretable and just as accurate.
Solution
ARIMA/SARIMA train, tune, and predict scripts with shared utilities, run alongside the ML models for a fair comparison on the same data.
Links
Deliverables
train_timeseries.py— fit ARIMA/SARIMA modelsoptimize_timeseries.py— order and seasonal-order searchpredict_timeseries.py— forecast generationts_common.py— shared time-series helpers
What's Included
The vertex/models/timeseries directory: train_timeseries.py, optimize_timeseries.py, predict_timeseries.py, and ts_common.py for shared seasonality and differencing helpers.
Strategic Context
Classical time-series methods are unfashionable and frequently correct. For clean, seasonal demand, a SARIMA model is interpretable, cheap, and competitive. Keeping it in the lineup guards against the reflex to reach for complexity the data doesn't require.
FAQs
When do classical methods win?
On clean, strongly seasonal series with limited exogenous drivers, a well-specified SARIMA is often interpretable and competitive.
Is Prophet included?
This template focuses on ARIMA/SARIMA via statsmodels; the structure makes adding Prophet straightforward.
Technical Architecture
Classical time-series methods are unfashionable and frequently correct. For clean, seasonal demand, a SARIMA model is interpretable, cheap, and competitive. Keeping it in the lineup guards against the reflex to reach for complexity the data doesn't require.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4