Forecast Model

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version: "1.0.0"

name: Prophet

description: "Tool for producing high quality forecasts for time series data that has multiple seasonality with li forecast-model, python, forecasting, python."

---

# Forecast Model

Forecast Model v2.0.0 — an AI toolkit for managing forecasting workflows from the command line. Log configurations, benchmarks, prompts, evaluations, fine-tuning runs, cost tracking, and optimization notes. Each entry is timestamped and persisted locally. Works entirely offline — your data never leaves your machine.

Why Forecast Model?

Commands

Domain Commands

Each domain command works in two modes: **log mode** (with arguments) saves a timestamped entry, **view mode** (no arguments) shows the 20 most recent entries.

| Command | Description |

|---------|-------------|

| `forecast-model configure <input>` | Log a configuration note such as model parameters, seasonal settings, or data pipeline configurations. Use this to track which settings were active during each forecasting experiment. |

| `forecast-model benchmark <input>` | Log a benchmark result or performance observation. Record MAE, RMSE, MAPE, or other accuracy metrics to compare forecast quality across model versions and datasets. |

| `forecast-model compare <input>` | Log a comparison note between models, methods, or configurations. Useful for side-by-side evaluations like Prophet vs ARIMA on specific time series datasets. |

| `forecast-model prompt <input>` | Log a prompt template or prompt engineering note. Track iterations on how you frame forecasting tasks for LLM-based approaches or hybrid models. |

| `forecast-model evaluate <input>` | Log an evaluation result or quality metric. Record accuracy scores, confidence intervals, prediction intervals, or backtesting results from forecast runs. |

| `forecast-model fine-tune <input>` | Log a fine-tuning run or hyperparameter note. Track changepoint settings, seasonality orders, holiday effects, and the resulting forecast accuracy. |

| `forecast-model analyze <input>` | Log an analysis observation or insight. Record trend patterns, seasonality decomposition results, anomaly detections, or data quality issues found. |

| `forecast-model cost <input>` | Log cost tracking data including compute expenses, API costs for cloud-based forecasting, and resource consumption. Essential for budget monitoring across forecast pipelines. |

| `forecast-model usage <input>` | Log usage metrics or consumption data. Track how many forecasts were generated, data volumes processed, and resource utilization patterns. |

| `forecast-model optimize <input>` | Log optimization attempts or improvements. Record hyperparameter tuning results, feature engineering experiments, and their impact on forecast accuracy. |

| `forecast-model test <input>` | Log test results or test case notes. Record backtesting outcomes, hold-out set performance, and cross-validation results from forecast models. |

| `forecast-model report <input>` | Log a report entry or summary finding. Capture weekly forecast accuracy summaries, model comparison reports, or stakeholder-ready findings. |

Utility Commands

| Command | Description |

|---------|-------------|

| `forecast-model stats` | Show summary statistics across all log files, including entry counts per category and total data size on disk. |

| `forecast-model export <fmt>` | Export all data to a file in the specified format. Supported formats: `json`, `csv`, `txt`. Output is saved to the data directory. |

| `forecast-model search <term>` | Search all log entries for a term using case-insensitive matching. Results are grouped by log category for easy scanning. |

| `forecast-model recent` | Show the 20 most recent entries from the unified activity log, giving a quick overview of recent work across all commands. |

| `forecast-model status` | Health check showing version, data directory path, total entry count, disk usage, and last activity timestamp. |

| `forecast-model help` | Show the built-in help message listing all available commands and usage information. |

| `forecast-model version` | Print the current version (v2.0.0). |

Data Storage

All data is stored locally at `~/.local/share/forecast-model/`. Each domain command writes to its own log file (e.g., `configure.log`, `benchmark.log`). A unified `history.log` tracks all actions across commands. Use `export` to back up your data at any time.

Requirements

When to Use

Examples

# Log a new configuration
forecast-model configure "ARIMA(2,1,2) with seasonal=True, period=7, dataset=retail_sales"

# Record a benchmark result
forecast-model benchmark "MAE=0.032, RMSE=0.048 on test set, 10k samples, horizon=14 days"

# Compare two models
forecast-model compare "Prophet vs ARIMA: Prophet wins on weekly data by 12% MAPE reduction"

# Track costs
forecast-model cost "Cloud forecasting API: $4.20 for 3 batch runs, 50k data points each"

# Search across all logs
forecast-model search "Prophet"

# Export everything as JSON
forecast-model export json

# Check overall status
forecast-model status

# View recent activity
forecast-model recent

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