{"library":"skforecast","title":"Skforecast","description":"Skforecast is a Python library for time series forecasting using statistical and machine learning models. It works with any estimator compatible with the scikit-learn API, including LightGBM, XGBoost, CatBoost, Keras, and many others. Current version is 0.22.0, with a release cadence of roughly quarterly.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install skforecast"],"cli":null},"imports":["from skforecast.recursive import ForecasterAutoreg","from skforecast.direct import ForecasterAutoregDirect","from skforecast.recursive import ForecasterAutoregCustom","from skforecast.recursive import ForecasterAutoregMultiOutput","from skforecast.sarimax import ForecasterSARIMAX","from skforecast.model_selection import BacktestGridSearch","from skforecast.model_selection import BacktestRandomSearch","from skforecast.preprocessing import TimeSeriesDifferentiator"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nimport pandas as pd\nfrom skforecast.recursive import ForecasterAutoreg\nfrom sklearn.ensemble import RandomForestRegressor\n\n# Example data\ny = pd.Series(np.random.rand(100), index=pd.date_range(start='2020-01-01', periods=100, freq='D'))\n\n# Create forecaster\nforecaster = ForecasterAutoreg(\n    regressor=RandomForestRegressor(random_state=123),\n    lags=10\n)\n\n# Fit\nforecaster.fit(y=y)\n\n# Predict\npredictions = forecaster.predict(steps=5)\nprint(predictions.head())","lang":"python","description":"Basic usage: create a ForecasterAutoreg with a random forest regressor and 10 lags, fit on a univariate series, and predict 5 steps ahead.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}