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PowerForecast Quick Reference

Wizard Steps

Step Name Key Action
1 Business Goal Select what to forecast (demand, workload, revenue, etc.)
2 Time Configuration Set frequency, horizon, confidence level
3 Data Source Upload CSV or JSON file
4 Model Selection Choose Auto or Manual model selection
5 Review & Run Validate and submit forecast

Business Targets

Target Icon Use For
Demand πŸ“¦ Product demand, sales volume, order quantities
Workload πŸ‘₯ Staff requirements, task volume, capacity needs
Revenue πŸ’° Sales revenue, income projections
Claims πŸ“‹ Insurance claims, requests, incidents
Transactions πŸ”„ Transaction volume, payments
Custom βš™οΈ Define your own target

Forecast Frequencies

Frequency Code Best For
Hourly H High-frequency data, real-time monitoring
Daily D Daily operations, daily sales
Weekly W Weekly reports, weekly demand
Monthly M Monthly planning, budget forecasts
Quarterly Q Quarterly business reviews
Yearly Y Annual planning, long-term trends

Confidence Levels

Level Use For
80% Less conservative, narrower intervals
90% Standard business planning
95% Default - balanced confidence
99% High-stakes decisions, risk-averse scenarios

Scenarios

Scenario Icon Description
Baseline πŸ“Š Most likely outcome (default)
Optimistic πŸ“ˆ Best-case scenario
Pessimistic πŸ“‰ Worst-case scenario
Stress ⚠️ Stress testing, extreme conditions

Model Selection Modes

Mode Icon Description
Automatic πŸ€– PowerForecast analyzes data and selects best models
Manual 🎯 Choose specific models (AutoARIMA, ETS, SeasonalNaive)

Available Models

Model Best For
AutoARIMA Automatic ARIMA selection, handles trends and seasonality
ETS Exponential Smoothing, handles various patterns
SeasonalNaive Simple seasonal patterns, baseline comparison

API Quick Reference

Forecast Engine API (Port 8020)

```bash

Health check

curl http://localhost:8020/health

Run forecast

curl -X POST http://localhost:8020/api/powerforecast/v1/forecast \

-H "Content-Type: application/json" \

-d @forecast-spec.json

List available models

curl http://localhost:8020/api/powerforecast/v1/models

```

SQLite API (Port 8021)

```bash

Health check

curl http://localhost:8021/health

List specifications

curl http://localhost:8021/api/forecast/specifications

Get run details

curl http://localhost:8021/api/forecast/runs/{runId}

Get results

curl http://localhost:8021/api/forecast/results/{runId}

Audit logs

curl http://localhost:8021/api/forecast/audit

```

Data Format

CSV Format

```csv

unique_id,ds,y

product_1,2024-01-01,100

product_1,2024-02-01,120

product_2,2024-01-01,50

product_2,2024-02-01,55

```

JSON Format

```json

[

{

"unique_id": "product_1",

"ds": ["2024-01-01", "2024-02-01", "2024-03-01"],

"y": [100, 120, 105]

},

{

"unique_id": "product_2",

"ds": ["2024-01-01", "2024-02-01", "2024-03-01"],

"y": [50, 55, 52]

}

]

```

Required Fields:

  • `unique_id`: Series identifier (string)
  • `ds`: Date array (ISO format: YYYY-MM-DD)
  • `y`: Value array (numbers)
  • Keyboard Shortcuts

    Shortcut Action
    `Ctrl+S` Save specification
    `Escape` Close modal
    `Enter` Confirm / Next step

    Troubleshooting Quick Fixes

    Problem Solution
    Data upload fails Check CSV/JSON format matches expected structure
    Forecast returns errors Verify data has enough points (minimum 2Γ—seasonal period)
    Models not selecting Check data quality - missing values or outliers may affect selection
    API offline Check ports 8020 (engine) and 8021 (SQLite API)
    Poor forecast quality Review diagnostics, consider data preprocessing or different models

    File Locations

    Component Path
    Forecast Engine API `forecast-api/`
    SQLite API `forecast-sqlite-api/`
    Wizard `forecast-wizard/`
    Admin Console `forecast-admin/`
    Templates `forecast-templates/`
    Test Datasets `test-datasets/`
    Documentation `documentation/`

    Environment URLs

    Environment Wizard Engine API SQLite API
    Local `file:///D:/PowerForecast/forecast-wizard/index.html` localhost:8020 localhost:8021
    Production forecast.planningpowertools.com api.planningpowertools.com config.planningpowertools.com

    Data Quality Options

    Option Description
    Handle Missing Values Automatically interpolate missing data points
    Handle Outliers Detect and handle outliers using IQR method

    Forecast Output Files

    After running a forecast, you can download:

    1. Specification File (forecast_specification_{runId}.json) - Complete spec with data for re-running

    2. Results File (forecast_results_{runId}.json) - Forecast results with actual data

    3. Diagnostics File (forecast_diagnostics_{runId}.json) - Quality assessment and recommendations

    4. Profiling File (forecast_profiling_{runId}.json) - Statistical profiling details

    Support

  • **Docs**: `documentation/`
  • **Email**: support@planningpowertools.com
  • **Swagger**: http://localhost:8020/docs (Engine) | http://localhost:8021/docs (SQLite API)