Build time features from dates and events. Generate lags, rolling metrics, and cyclic encodings safely. Reduce leakage risks and improve forecast model stability today.
Sample temporal dataset used for feature planning.
| Timestamp | Target | Temperature | Promo Flag | Holiday Flag |
|---|---|---|---|---|
| 2026-01-01 00:00 | 142 | 17.2 | 0 | 1 |
| 2026-01-01 01:00 | 138 | 16.9 | 0 | 1 |
| 2026-01-01 02:00 | 133 | 16.4 | 0 | 1 |
| 2026-01-01 03:00 | 129 | 16.1 | 0 | 1 |
| 2026-01-01 04:00 | 127 | 15.8 | 1 | 1 |
| 2026-01-01 05:00 | 131 | 15.6 | 1 | 1 |
1) Cleaned rows = Total observations × (1 − Missing rate) × (1 − Duplicate rate)
2) Burn-in rows = max(Lag count, Rolling window − 1) + Forecast horizon + Validation gap
3) Usable rows = max(Cleaned rows − Burn-in rows, 0)
4) Lag features = Target columns × Lag count
5) Rolling features = (Target + Exogenous columns) × Rolling stats per column
6) Cyclical features = Cyclical parts × 2 (sine and cosine)
7) Event features = Event columns × 3 (flag, lead, lag placeholders)
8) Total features = Lag + Rolling + Calendar + Cyclical + Event features
9) Feature density = Total features ÷ Usable rows
10) Memory estimate (MB) = Usable rows × Total features × 8 ÷ 1,048,576
11) Leakage risk score is a weighted score based on centered windows, horizon-to-lag ratio, training split, and validation gap coverage.
Temporal feature engineering converts timestamps into model ready predictors for forecasting workflows. This calculator estimates usable rows, burn in loss, and total feature volume before implementation. Analysts can compare lag depth, rolling windows, and calendar settings using one structured form. The output supports demand planning, anomaly detection, and scheduling use cases. Early sizing reduces rework and improves consistency across experimentation, validation, and deployment. Workflow governance remains clearer for stakeholders and reviewers.
Lag features capture historical behavior and often provide strong predictive signal in time series tasks. Rolling statistics add trend, variability, and level context, but they increase dimensionality quickly. This calculator quantifies that tradeoff using lag count, window size, and selected statistics per column. Teams can estimate feature growth before training begins, avoid oversized matrices, and keep experiments efficient, stable, and easier to reproduce in practice. This improves planning quality across teams and.
Calendar features explain recurring patterns such as weekday effects, month changes, quarter shifts, and seasonality. Cyclical encoding transforms periodic values into sine and cosine pairs, preserving continuity at boundaries. That keeps hour twenty three close to hour zero in model space. The calculator combines calendar and cyclical choices into a practical feature estimate and supports interpretable temporal design for robust forecasting and monitoring pipelines. It also reduces unnecessary columns during feature iteration.
Temporal leakage inflates performance when future information enters the training matrix. Common causes include centered rolling windows, weak validation gaps, and lag settings that do not cover the forecast horizon. This calculator estimates leakage exposure with a weighted score tied to horizon, lag depth, split percentage, and gap choices. Teams can adjust feature rules early and align experiments with realistic production behavior before modeling starts. Leakage awareness improves trust in validation and.
Production forecasting needs predictable memory use and repeatable data preparation steps. This calculator estimates memory demand from usable rows and generated features using a simple numeric assumption. It also separates training and holdout rows for experiment planning and resource checks. These outputs help teams size compute resources, schedule pipelines, and design feature stores with fewer surprises during deployment and maintenance activities. Structured estimates support reproducibility budgeting and operational readiness. for scaling and governance.
Usable rows are the records remaining after cleaning and burn in subtraction, where all requested lags and rolling features can be computed safely.
Each cyclical field creates sine and cosine columns. The pair preserves circular continuity and improves learning around time boundaries.
No. It is a planning tool for temporal feature sizing, leakage checks, row availability, and memory estimation before training.
Centered windows, small validation gaps, and forecast horizons that exceed lag coverage typically increase leakage risk and unstable validation results.
Yes. Add exogenous columns to estimate rolling feature growth for weather, pricing, traffic, operations, or other external signals.
Reduce features when usable rows are limited, memory usage grows sharply, or validation performance becomes unstable from excessive complexity.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.