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      "date": "2012-02-02"
    },
    {
      "version": "3.18",
      "date": "2012-02-17"
    },
    {
      "version": "3.19",
      "date": "2012-02-22"
    },
    {
      "version": "3.20",
      "date": "2012-04-02"
    },
    {
      "version": "3.21",
      "date": "2012-04-30"
    },
    {
      "version": "3.22",
      "date": "2012-06-07"
    },
    {
      "version": "3.23",
      "date": "2012-07-18"
    },
    {
      "version": "3.24",
      "date": "2012-07-23"
    },
    {
      "version": "3.25",
      "date": "2012-09-11"
    },
    {
      "version": "4.00",
      "date": "2012-11-27"
    },
    {
      "version": "4.01",
      "date": "2013-01-22"
    },
    {
      "version": "4.02",
      "date": "2013-03-06"
    },
    {
      "version": "4.03",
      "date": "2013-03-17"
    },
    {
      "version": "4.04",
      "date": "2013-04-22"
    },
    {
      "version": "4.05",
      "date": "2013-06-19"
    },
    {
      "version": "4.06",
      "date": "2013-06-30"
    },
    {
      "version": "4.7",
      "date": "2013-09-27"
    },
    {
      "version": "4.8",
      "date": "2013-09-30"
    },
    {
      "version": "5.0",
      "date": "2014-01-17"
    },
    {
      "version": "5.1",
      "date": "2014-02-08"
    },
    {
      "version": "5.2",
      "date": "2014-02-24"
    },
    {
      "version": "5.3",
      "date": "2014-03-24"
    },
    {
      "version": "5.4",
      "date": "2014-05-08"
    },
    {
      "version": "5.5",
      "date": "2014-08-12"
    },
    {
      "version": "5.6",
      "date": "2014-09-24"
    },
    {
      "version": "5.7",
      "date": "2014-12-17"
    },
    {
      "version": "5.8",
      "date": "2015-01-06"
    },
    {
      "version": "5.9",
      "date": "2015-02-26"
    },
    {
      "version": "6.0",
      "date": "2015-05-09"
    },
    {
      "version": "6.1",
      "date": "2015-05-12"
    },
    {
      "version": "6.2",
      "date": "2015-10-20"
    },
    {
      "version": "7.0",
      "date": "2016-04-03"
    },
    {
      "version": "7.1",
      "date": "2016-04-14"
    },
    {
      "version": "7.2",
      "date": "2016-09-09"
    },
    {
      "version": "7.3",
      "date": "2016-10-12"
    },
    {
      "version": "8.0",
      "date": "2017-02-23"
    },
    {
      "version": "8.1",
      "date": "2017-06-17"
    },
    {
      "version": "8.2",
      "date": "2017-09-25"
    },
    {
      "version": "8.3",
      "date": "2018-04-11"
    },
    {
      "version": "8.4",
      "date": "2018-06-21"
    },
    {
      "version": "8.5",
      "date": "2019-01-18"
    },
    {
      "version": "8.6",
      "date": "2019-04-16"
    },
    {
      "version": "8.7",
      "date": "2019-04-29"
    },
    {
      "version": "8.8",
      "date": "2019-08-02"
    },
    {
      "version": "8.9",
      "date": "2019-08-22"
    },
    {
      "version": "8.10",
      "date": "2019-12-05"
    },
    {
      "version": "8.11",
      "date": "2020-02-09"
    },
    {
      "version": "8.12",
      "date": "2020-03-31"
    },
    {
      "version": "8.13",
      "date": "2020-09-12"
    },
    {
      "version": "8.14",
      "date": "2021-03-11"
    },
    {
      "version": "8.15",
      "date": "2021-06-01"
    },
    {
      "version": "8.16",
      "date": "2022-01-10"
    },
    {
      "version": "8.17.0",
      "date": "2022-07-25"
    },
    {
      "version": "8.18",
      "date": "2022-10-02"
    },
    {
      "version": "8.19",
      "date": "2022-11-21"
    },
    {
      "version": "8.20",
      "date": "2023-01-06"
    },
    {
      "version": "8.21",
      "date": "2023-02-27"
    },
    {
      "version": "8.21.1",
      "date": "2023-08-31"
    },
    {
      "version": "8.22.0",
      "date": "2024-03-04"
    },
    {
      "version": "8.23.0",
      "date": "2024-06-20"
    },
    {
      "version": "8.24.0",
      "date": "2025-04-08"
    },
    {
      "version": "9.0.0",
      "date": "2026-01-11"
    },
    {
      "version": "9.0.1",
      "date": "2026-02-16"
    },
    {
      "version": "9.0.2",
      "date": "2026-03-18"
    }
  ],
  "_exports": [
    "%>%",
    "accuracy",
    "Acf",
    "arfima",
    "Arima",
    "arima.errors",
    "arimaorder",
    "auto.arima",
    "autolayer",
    "autoplot",
    "baggedETS",
    "baggedModel",
    "bats",
    "bizdays",
    "bld.mbb.bootstrap",
    "BoxCox",
    "BoxCox.lambda",
    "Ccf",
    "checkresiduals",
    "croston",
    "croston_model",
    "CV",
    "CVar",
    "dm.test",
    "dshw",
    "easter",
    "ets",
    "findfrequency",
    "forecast",
    "forecast.ets",
    "fourier",
    "fourierf",
    "geom_forecast",
    "GeomForecast",
    "getResponse",
    "ggAcf",
    "ggCcf",
    "gghistogram",
    "gglagchull",
    "gglagplot",
    "ggmonthplot",
    "ggPacf",
    "ggseasonplot",
    "ggsubseriesplot",
    "ggtaperedacf",
    "ggtaperedpacf",
    "ggtsdisplay",
    "holt",
    "hw",
    "InvBoxCox",
    "is.acf",
    "is.Arima",
    "is.baggedModel",
    "is.bats",
    "is.constant",
    "is.ets",
    "is.forecast",
    "is.mforecast",
    "is.modelAR",
    "is.nnetar",
    "is.nnetarmodels",
    "is.splineforecast",
    "is.stlm",
    "ma",
    "mean_model",
    "meanf",
    "modelAR",
    "modeldf",
    "monthdays",
    "mstl",
    "msts",
    "na.interp",
    "naive",
    "ndiffs",
    "nnetar",
    "nsdiffs",
    "ocsb.test",
    "Pacf",
    "remainder",
    "rw_model",
    "rwf",
    "seasadj",
    "seasonal",
    "seasonaldummy",
    "seasonaldummyf",
    "seasonplot",
    "ses",
    "sindexf",
    "snaive",
    "spline_model",
    "splinef",
    "StatForecast",
    "stlf",
    "stlm",
    "taperedacf",
    "taperedpacf",
    "tbats",
    "tbats.components",
    "theta_model",
    "thetaf",
    "trendcycle",
    "tsclean",
    "tsCV",
    "tsdisplay",
    "tslm",
    "tsoutliers"
  ],
  "_datasets": [
    {
      "name": "gas",
      "title": "Australian monthly gas production",
      "object": "gas",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "gold",
      "title": "Daily morning gold prices",
      "object": "gold",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "taylor",
      "title": "Half-hourly electricity demand",
      "object": "taylor",
      "class": [
        "msts",
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "wineind",
      "title": "Australian total wine sales",
      "object": "wineind",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "woolyrnq",
      "title": "Quarterly production of woollen yarn in Australia",
      "object": "woolyrnq",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "accuracy.forecast",
      "title": "Accuracy measures for a forecast model",
      "topics": [
        "accuracy.Arima",
        "accuracy.fc_model",
        "accuracy.forecast",
        "accuracy.lm",
        "accuracy.mforecast",
        "accuracy.numeric",
        "accuracy.ts"
      ]
    },
    {
      "page": "Acf",
      "title": "(Partial) Autocorrelation and Cross-Correlation Function Estimation",
      "topics": [
        "Acf",
        "Ccf",
        "Pacf",
        "taperedacf",
        "taperedpacf"
      ]
    },
    {
      "page": "arfima",
      "title": "Fit a fractionally differenced ARFIMA model",
      "topics": [
        "arfima"
      ]
    },
    {
      "page": "Arima",
      "title": "Fit ARIMA model to univariate time series",
      "topics": [
        "Arima",
        "as.character.Arima",
        "print.ARIMA",
        "summary.Arima"
      ]
    },
    {
      "page": "arima.errors",
      "title": "Errors from a regression model with ARIMA errors",
      "topics": [
        "arima.errors"
      ]
    },
    {
      "page": "arimaorder",
      "title": "Return the order of an ARIMA or ARFIMA model",
      "topics": [
        "arimaorder"
      ]
    },
    {
      "page": "auto.arima",
      "title": "Fit best ARIMA model to univariate time series",
      "topics": [
        "auto.arima"
      ]
    },
    {
      "page": "autoplot.ts",
      "title": "Automatically create a ggplot for time series objects",
      "topics": [
        "autolayer.msts",
        "autolayer.mts",
        "autolayer.ts",
        "autoplot.msts",
        "autoplot.mts",
        "autoplot.ts",
        "fortify.ts"
      ]
    },
    {
      "page": "autoplot.acf",
      "title": "ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting",
      "topics": [
        "autoplot.acf",
        "autoplot.mpacf",
        "ggAcf",
        "ggCcf",
        "ggPacf",
        "ggtaperedacf",
        "ggtaperedpacf"
      ]
    },
    {
      "page": "autoplot.seas",
      "title": "Plot time series decomposition components using ggplot",
      "topics": [
        "autoplot.decomposed.ts",
        "autoplot.mstl",
        "autoplot.seas",
        "autoplot.stl",
        "autoplot.StructTS"
      ]
    },
    {
      "page": "plot.mforecast",
      "title": "Multivariate forecast plot",
      "topics": [
        "autolayer.mforecast",
        "autoplot.mforecast",
        "plot.mforecast"
      ]
    },
    {
      "page": "baggedModel",
      "title": "Forecasting using a bagged model",
      "topics": [
        "baggedETS",
        "baggedModel",
        "print.baggedModel"
      ]
    },
    {
      "page": "bats",
      "title": "BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)",
      "topics": [
        "as.character.bats",
        "bats",
        "print.bats"
      ]
    },
    {
      "page": "bizdays",
      "title": "Number of trading days in each season",
      "topics": [
        "bizdays"
      ]
    },
    {
      "page": "bld.mbb.bootstrap",
      "title": "Box-Cox and Loess-based decomposition bootstrap.",
      "topics": [
        "bld.mbb.bootstrap"
      ]
    },
    {
      "page": "BoxCox",
      "title": "Box Cox Transformation",
      "topics": [
        "BoxCox",
        "InvBoxCox"
      ]
    },
    {
      "page": "BoxCox.lambda",
      "title": "Automatic selection of Box Cox transformation parameter",
      "topics": [
        "BoxCox.lambda"
      ]
    },
    {
      "page": "checkresiduals",
      "title": "Check that residuals from a time series model look like white noise",
      "topics": [
        "checkresiduals"
      ]
    },
    {
      "page": "croston_model",
      "title": "Croston forecast model",
      "topics": [
        "croston_model"
      ]
    },
    {
      "page": "CV",
      "title": "Cross-validation statistic",
      "topics": [
        "CV"
      ]
    },
    {
      "page": "CVar",
      "title": "k-fold Cross-Validation applied to an autoregressive model",
      "topics": [
        "CVar",
        "print.CVar"
      ]
    },
    {
      "page": "dm.test",
      "title": "Diebold-Mariano test for predictive accuracy",
      "topics": [
        "dm.test"
      ]
    },
    {
      "page": "dshw",
      "title": "Double-Seasonal Holt-Winters Forecasting",
      "topics": [
        "dshw"
      ]
    },
    {
      "page": "easter",
      "title": "Easter holidays in each season",
      "topics": [
        "easter"
      ]
    },
    {
      "page": "ets",
      "title": "Exponential smoothing state space model",
      "topics": [
        "as.character.ets",
        "coef.ets",
        "ets",
        "print.ets",
        "summary.ets",
        "tsdiag.ets"
      ]
    },
    {
      "page": "findfrequency",
      "title": "Find dominant frequency of a time series",
      "topics": [
        "findfrequency"
      ]
    },
    {
      "page": "fitted.Arima",
      "title": "h-step in-sample forecasts for time series models.",
      "topics": [
        "fitted.ar",
        "fitted.ARFIMA",
        "fitted.Arima",
        "fitted.bats",
        "fitted.ets",
        "fitted.forecast_ARIMA",
        "fitted.modelAR",
        "fitted.nnetar",
        "fitted.tbats"
      ]
    },
    {
      "page": "forecast.baggedModel",
      "title": "Forecasting using a bagged model",
      "topics": [
        "forecast.baggedModel"
      ]
    },
    {
      "page": "forecast.bats",
      "title": "Forecasting using BATS and TBATS models",
      "topics": [
        "forecast.bats",
        "forecast.tbats"
      ]
    },
    {
      "page": "forecast.croston_model",
      "title": "Forecasts for intermittent demand using Croston's method",
      "topics": [
        "croston",
        "forecast.croston_model"
      ]
    },
    {
      "page": "forecast.ets",
      "title": "Forecasting using ETS models",
      "topics": [
        "forecast.ets"
      ]
    },
    {
      "page": "forecast.Arima",
      "title": "Forecasting using ARIMA or ARFIMA models",
      "topics": [
        "forecast.ar",
        "forecast.Arima",
        "forecast.forecast_ARIMA",
        "forecast.fracdiff"
      ]
    },
    {
      "page": "forecast.HoltWinters",
      "title": "Forecasting using Holt-Winters objects",
      "topics": [
        "forecast.HoltWinters"
      ]
    },
    {
      "page": "forecast.lm",
      "title": "Forecast a linear model with possible time series components",
      "topics": [
        "forecast.lm"
      ]
    },
    {
      "page": "forecast.mean_model",
      "title": "Mean Forecast",
      "topics": [
        "forecast.mean_model",
        "meanf"
      ]
    },
    {
      "page": "forecast.mlm",
      "title": "Forecast a multiple linear model with possible time series components",
      "topics": [
        "forecast.mlm"
      ]
    },
    {
      "page": "forecast.modelAR",
      "title": "Forecasting using user-defined model",
      "topics": [
        "forecast.modelAR"
      ]
    },
    {
      "page": "forecast.mts",
      "title": "Forecasting time series",
      "topics": [
        "as.data.frame.mforecast",
        "forecast.mts",
        "mforecast",
        "print.mforecast",
        "summary.mforecast"
      ]
    },
    {
      "page": "forecast.nnetar",
      "title": "Forecasting using neural network models",
      "topics": [
        "forecast.nnetar"
      ]
    },
    {
      "page": "forecast.rw_model",
      "title": "Naive and Random Walk Forecasts",
      "topics": [
        "forecast.rw_model",
        "naive",
        "rwf",
        "snaive"
      ]
    },
    {
      "page": "forecast.spline_model",
      "title": "Returns local linear forecasts and prediction intervals using cubic smoothing splines estimated with 'spline_model()'.",
      "topics": [
        "forecast.spline_model",
        "splinef"
      ]
    },
    {
      "page": "forecast.stl",
      "title": "Forecasting using stl objects",
      "topics": [
        "forecast.stl",
        "forecast.stlm",
        "stlf"
      ]
    },
    {
      "page": "forecast.StructTS",
      "title": "Forecasting using Structural Time Series models",
      "topics": [
        "forecast.StructTS"
      ]
    },
    {
      "page": "forecast.theta_model",
      "title": "Theta method forecasts.",
      "topics": [
        "forecast.theta_model",
        "thetaf"
      ]
    },
    {
      "page": "forecast.ts",
      "title": "Forecasting time series",
      "topics": [
        "as.data.frame.forecast",
        "as.ts.forecast",
        "forecast.default",
        "forecast.ts",
        "print.forecast",
        "summary.forecast"
      ]
    },
    {
      "page": "fourier",
      "title": "Fourier terms for modelling seasonality",
      "topics": [
        "fourier",
        "fourierf"
      ]
    },
    {
      "page": "gas",
      "title": "Australian monthly gas production",
      "topics": [
        "gas"
      ]
    },
    {
      "page": "getResponse",
      "title": "Get response variable from time series model.",
      "topics": [
        "getResponse",
        "getResponse.ar",
        "getResponse.Arima",
        "getResponse.baggedModel",
        "getResponse.bats",
        "getResponse.default",
        "getResponse.fracdiff",
        "getResponse.lm",
        "getResponse.mforecast",
        "getResponse.tbats"
      ]
    },
    {
      "page": "gghistogram",
      "title": "Histogram with optional normal and kernel density functions",
      "topics": [
        "gghistogram"
      ]
    },
    {
      "page": "gglagplot",
      "title": "Time series lag ggplots",
      "topics": [
        "gglagchull",
        "gglagplot"
      ]
    },
    {
      "page": "ggmonthplot",
      "title": "Create a seasonal subseries ggplot",
      "topics": [
        "ggmonthplot",
        "ggsubseriesplot"
      ]
    },
    {
      "page": "seasonplot",
      "title": "Seasonal plot",
      "topics": [
        "ggseasonplot",
        "seasonplot"
      ]
    },
    {
      "page": "tsdisplay",
      "title": "Time series display",
      "topics": [
        "ggtsdisplay",
        "tsdisplay"
      ]
    },
    {
      "page": "gold",
      "title": "Daily morning gold prices",
      "topics": [
        "gold"
      ]
    },
    {
      "page": "is.ets",
      "title": "Is an object a particular model type?",
      "topics": [
        "is.acf",
        "is.Arima",
        "is.baggedModel",
        "is.bats",
        "is.ets",
        "is.modelAR",
        "is.nnetar",
        "is.nnetarmodels",
        "is.stlm"
      ]
    },
    {
      "page": "is.constant",
      "title": "Is an object constant?",
      "topics": [
        "is.constant"
      ]
    },
    {
      "page": "is.forecast",
      "title": "Is an object a particular forecast type?",
      "topics": [
        "is.forecast",
        "is.mforecast",
        "is.splineforecast"
      ]
    },
    {
      "page": "ma",
      "title": "Moving-average smoothing",
      "topics": [
        "ma"
      ]
    },
    {
      "page": "mean_model",
      "title": "Mean Forecast Model",
      "topics": [
        "mean_model"
      ]
    },
    {
      "page": "modelAR",
      "title": "Time Series Forecasts with a user-defined model",
      "topics": [
        "modelAR",
        "print.modelAR"
      ]
    },
    {
      "page": "modeldf",
      "title": "Compute model degrees of freedom",
      "topics": [
        "modeldf"
      ]
    },
    {
      "page": "monthdays",
      "title": "Number of days in each season",
      "topics": [
        "monthdays"
      ]
    },
    {
      "page": "mstl",
      "title": "Multiple seasonal decomposition",
      "topics": [
        "mstl"
      ]
    },
    {
      "page": "msts",
      "title": "Multi-Seasonal Time Series",
      "topics": [
        "msts",
        "print.msts",
        "window.msts",
        "`[.msts`"
      ]
    },
    {
      "page": "na.interp",
      "title": "Interpolate missing values in a time series",
      "topics": [
        "na.interp"
      ]
    },
    {
      "page": "ndiffs",
      "title": "Number of differences required for a stationary series",
      "topics": [
        "ndiffs"
      ]
    },
    {
      "page": "nnetar",
      "title": "Neural Network Time Series Forecasts",
      "topics": [
        "nnetar",
        "print.nnetar",
        "print.nnetarmodels"
      ]
    },
    {
      "page": "nsdiffs",
      "title": "Number of differences required for a seasonally stationary series",
      "topics": [
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      "source": "JSS2008.Rmd",
      "filename": "JSS2008.html",
      "title": "Automatic Time Series Forecasting: the forecast Package for R",
      "author": "Rob J Hyndman, Yeasmin Khandakar",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Exponential smoothing",
        "Point forecasts for all methods",
        "Innovations state space models",
        "State space models for all exponential smoothing methods",
        "Estimation",
        "Model selection",
        "Automatic forecasting",
        "ARIMA models",
        "Choosing the model order using unit root tests and the AIC",
        "A step-wise procedure for traversing the model space",
        "Comparisons with exponential smoothing",
        "The forecast package",
        "Implementation of the automatic exponential smoothing algorithm",
        "The HoltWinters() function",
        "Implementation of the automatic ARIMA algorithm",
        "The forecast() function",
        "Other functions",
        "Bibliography"
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      "created": "2017-02-11 00:34:43",
      "modified": "2026-01-10 05:30:52",
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