Skforecast-AI – Agentic time series forecasting in Python Skforecast-AI, a new Python package from the skforecast team, pairs a deterministic forecasting engine with an optional LLM reasoning layer to automate time series forecasting, returning both predictions and the runnable skforecast script that produced them. The package, requiring Python ≥ 3.10, profiles data, selects models, and evaluates performance in one call, supporting statistical and foundation models like Chronos-2, TimesFM, and Moirai. | Package | | skforecast-ai is an AI forecasting assistant that pairs a deterministic engine, powered by skforecast https://skforecast.org , with an LLM reasoning layer . Simply provide a time series, and the assistant automatically profiles the data, selects a model using established best practices, and evaluates its performance. It returns both the final forecast and the runnable skforecast script that produced it. Why skforecast-ai? -why-skforecast-ai Installation -installation Quickstart Python -quickstart-python Quickstart CLI -quickstart-cli How it works -how-it-works Documentation -documentation Contributing -contributing Citation -citation License -license - 🎯 Deterministic by design : built as a strict rule-based engine to guarantee absolute consistency, same input always means the same output. - 🔍 Code you can inspect : the script you see is the code that ran. Inspect it, version it, or run it standalone with plain skforecast . - ⚡ From data to forecast in one call : automatic data profiling, model and estimator selection, lag/feature engineering, and backtest evaluation. - 💻 Python or terminal : drive the full pipeline from a few lines of Python or from the command line. - 💬 LLM reasoning layer : explains the engine's decisions in plain language, helps you improve the configuration, and lets you ask for advice. This layer is entirely optional; the core forecasting pipeline can run fully offline. - 🏗️ Built on skforecast : recursive & direct forecasters, multi-series, statistical, and foundation models Chronos-2, TimesFM, Moirai, and more . Requires Python ≥ 3.10. pip install skforecast-ai To enable the optional LLM reasoning layer: pip install "skforecast-ai llm " Install from source for development git clone https://github.com/skforecast/skforecast-ai.git cd skforecast-ai pip install -e ". dev " From raw data to a validated forecast, and the code behind it, in a few lines: python import pandas as pd from skforecast ai import ForecastingAssistant from skforecast.datasets import load demo dataset data = load demo dataset verbose=False assistant = ForecastingAssistant result = assistant.forecast data=data, target="y", steps=12 print result.predictions forecast for the next 12 steps print result.metrics evaluation metrics: MAE, MSE, MASE print result.code the exact skforecast script that produced this result That single forecast call profiled the data, chose a forecaster and estimator, generated a skforecast script, and executed it. result.code is the script that ran. The returned ForecastResult exposes everything the pipeline produced: | Attribute | What it holds | |---|---| result.predictions | Forecast for the requested horizon includes interval columns when interval is requested | result.metrics | Backtest evaluation metrics MAE, MSE, MASE | result.code | The runnable skforecast script that produced the result | result.profile | What profiling detected about your data | result.plan | The forecaster, estimator, lags, and metrics that were chosen | 👉 New here? Walk through it step by step in Your first forecast . The same pipeline runs from the terminal. Point it at a CSV file or URL: End-to-end forecast profile → plan → code → forecast skforecast-ai forecast data.csv --target y --date-column datetime --steps 12 Just inspect the data skforecast-ai profile data.csv --target y --date-column datetime Generate a standalone, runnable script without executing it skforecast-ai forecast-code data.csv --target y --date-column datetime --steps 12 --output forecast.py Run skforecast-ai --help or skforecast-ai