Demand Forecasting
Forecast product demand: incorporate seasonality, promotions, holidays, price effects, and external factors. Use for inventory planning, capacity management, or revenue projection.
Install
$ agentstack add skill-ericwang915-data-scientist-skills-demand-forecasting ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ericwang915-data-scientist-skills-demand-forecasting) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Demand Forecasting
Purpose
Predict future demand incorporating multiple factors for accurate planning.
How It Works
Step 1: Analyze Historical Demand
- Trend, seasonality, cyclicality
- Promotional effects and holiday impacts
- External factors (weather, economic indicators)
Step 2: Choose Model
- Simple: Moving average, exponential smoothing
- Statistical: SARIMA, Prophet with regressors
- ML: XGBoost with lag features, LightGBM
- Hierarchy: Top-down, bottom-up, middle-out reconciliation
Step 3: Include External Factors
- Promotions and pricing changes
- Calendar events and holidays
- Competitor actions
- Economic indicators
Step 4: Evaluate
- Backtest with rolling origin cross-validation
- MAE, MAPE, WMAPE by product/region
- Bias detection (over/under-forecasting)
Usage Examples
"Forecast weekly demand for our top 50 products including holiday effects"Output Format
- Forecasts: Point predictions with confidence intervals
- Accuracy Metrics: Historical backtest results
- Factor Analysis: Impact of each driver on demand
- Python Code: Prophet / XGBoost implementation
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ericwang915
- Source: ericwang915/data-scientist-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.