Prophet

---

name: Prophet

description: "Forecast time-series data with seasonal trend modeling. Use when predicting sales, checking model accuracy, converting frequencies, analyzing growth."

version: "2.0.0"

license: MIT

runtime: python3

---

# Prophet

A utility toolkit for running, checking, converting, analyzing, and generating time-series forecasts. Record predictions, compare models, batch-process data, and generate reports — all from the command line with persistent local storage.

Quick Start

bash scripts/script.sh <command> [args...]

Commands

**Core Operations**

**Batch & Comparison**

**Configuration & Reporting**

**Utilities**

Each command accepts free-text input. When called without arguments, it displays the most recent 20 entries for that category.

> **Note:** The script has both a `status` subcommand (for recording status notes) and a built-in `_status` health-check function. Similarly, `export` serves as both a data-recording subcommand and a built-in export-to-file function.

Data Storage

All data is stored as plain-text log files in:

~/.local/share/prophet/
├── run.log           # Forecast run records
├── check.log         # Validation and accuracy checks
├── convert.log       # Data conversion records
├── analyze.log       # Analysis findings
├── generate.log      # Generated forecast data
├── preview.log       # Preview and dry-run results
├── batch.log         # Batch processing jobs
├── compare.log       # Model comparison data
├── export.log        # Export operation records
├── config.log        # Configuration changes
├── status.log        # Status observations
├── report.log        # Summary reports
└── history.log       # Unified activity history

Each entry is stored as `YYYY-MM-DD HH:MM|<input>` — one line per record. The `history.log` file tracks all commands chronologically.

Requirements

When to Use

1. **Tracking forecast experiments** — Use `run` and `check` to log forecast runs and validation results for systematic comparison over time

2. **Converting time-series frequencies** — Use `convert` to document frequency changes (daily → weekly, hourly → daily) and their impact on predictions

3. **Batch forecasting pipelines** — Use `batch` to record batch jobs across multiple datasets or product lines, then `compare` to contrast results

4. **Analyzing seasonal trends** — Use `analyze` to log observations about seasonality, growth patterns, and anomalies discovered during data exploration

5. **Building forecast reports** — Use `report` and `export json` to generate structured summaries for stakeholders, combining run results with configuration notes

Examples

# Run a forecast and record it
prophet run "Q3 2025 sales forecast: 12,500 units, MAPE 4.2%"

# Log a validation check
prophet check "Holdout test: predicted 8,200 vs actual 8,450, error 2.96%"

# Record a frequency conversion
prophet convert "Converted daily sales to weekly aggregates for smoother trend"

# Analyze seasonal patterns
prophet analyze "Strong weekly seasonality detected: peaks Mon/Tue, trough Sat/Sun"

# Compare two model configurations
prophet compare "Multiplicative vs additive seasonality: multiplicative MAPE 3.1% vs 4.7%"

# View summary statistics
prophet stats

# Export all data as CSV
prophet export csv

# Search for entries about a specific metric
prophet search "MAPE"

Configuration

Set `PROPHET_DIR` environment variable to override the default data directory. Default: `~/.local/share/prophet/`

Output

All commands output to stdout. Redirect to a file with `prophet <command> > output.txt`. Export formats (json, csv, txt) write to the data directory and report the output path and file size.

---

Powered by BytesAgain | bytesagain.com | hello@bytesagain.com