{
  "_comment": "Configuration template for the time-series-forecaster plugin.",
  "model_name": "Prophet",
  "_comment": "Name of the forecasting model to use. Options: Prophet, ARIMA, ExponentialSmoothing. Default: Prophet",
  "model_parameters": {
    "_comment": "Parameters specific to the chosen model.",
    "Prophet": {
      "_comment": "Parameters for the Prophet model (https://facebook.github.io/prophet/docs/parameters.html). Leave empty to use defaults.",
      "growth": "linear",
      "_comment": "Type of growth: 'linear' or 'logistic'.",
      "changepoints": null,
      "_comment": "List of dates at which to include potential changepoints. If None, potential changepoints are automatically placed.",
      "n_changepoints": 25,
      "_comment": "Number of potential changepoints to place automatically.",
      "changepoint_range": 0.8,
      "_comment": "Proportion of history in which trend changepoints will be estimated.",
      "yearly_seasonality": "auto",
      "_comment": "Fit yearly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.",
      "weekly_seasonality": "auto",
      "_comment": "Fit weekly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.",
      "daily_seasonality": "auto",
      "_comment": "Fit daily seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.",
      "holidays": null,
      "_comment": "Pandas DataFrame of holidays with columns 'ds' (date) and 'holiday' (holiday name).",
      "seasonality_mode": "additive",
      "_comment": "Type of seasonality: 'additive' or 'multiplicative'.",
      "seasonality_prior_scale": 10.0,
      "_comment": "Parameter modulating the strength of the seasonality model.",
      "holidays_prior_scale": 10.0,
      "_comment": "Parameter modulating the strength of the holiday model.",
      "changepoint_prior_scale": 0.05,
      "_comment": "Parameter modulating the flexibility of the automatic changepoint selection.",
      "mcmc_samples": 0,
      "_comment": "Number of MCMC samples to draw. If 0, will do point estimates.",
      "interval_width": 0.8,
      "_comment": "Width of the uncertainty intervals provided for the forecast.",
      "uncertainty_samples": 1000,
      "_comment": "Number of simulated draws used to estimate uncertainty intervals.",
      "stan_backend": null
      "_comment": "The Stan backend to use. Valid options are: null (default), 'CMDSTANPB', and 'PYSTAN'.",
    },
    "ARIMA": {
      "_comment": "Parameters for the ARIMA model (https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima.model.ARIMA.html).",
      "p": 5,
      "_comment": "The number of lag observations included in the model, also called the order of the AR part.",
      "d": 1,
      "_comment": "The number of times that the raw observations are differenced, also called the degree of differencing.",
      "q": 0,
      "_comment": "The size of the moving average window, also called the order of the MA part.",
      "seasonal_order": [
        0,
        0,
        0,
        0
      ],
      "_comment": "The (P,D,Q,s) order of the seasonal component of the model for seasonality of period s (often 4 or 12). Can be set to None if there is no seasonal component.",
      "trend": null,
      "_comment": "Parameter controlling the deterministic trend. Can be a string ('n', 'c', 't', 'ct') or a list of regressor names. 'n' - no trend, 'c' - constant only, 't' - time trend only, 'ct' - constant and time trend."
    },
    "ExponentialSmoothing": {
      "_comment": "Parameters for the Exponential Smoothing model (https://www.statsmodels.org/stable/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html).",
      "seasonal_periods": 12,
      "_comment": "The number of periods in a complete seasonal cycle.",
      "trend": "add",
      "_comment": "Type of trend component. Options: 'add', 'mul', None.",
      "seasonal": "add",
      "_comment": "Type of seasonal component. Options: 'add', 'mul', None.",
      "damped_trend": false,
      "_comment": "Should the trend component be damped?",
      "initialization_method": "estimated",
      "_comment": "Method for initializing the recursions. Options: 'estimated', 'heuristic', 'known'.",
      "use_boxcox": false,
      "_comment": "Should the Box-Cox transform be applied to the data?"
    }
  },
  "data_frequency": "D",
  "_comment": "Frequency of the time series data. Options: 'D' (daily), 'W' (weekly), 'M' (monthly), 'Q' (quarterly), 'Y' (yearly), etc.",
  "forecast_horizon": 30,
  "_comment": "Number of periods to forecast into the future.",
  "confidence_interval": 0.95,
  "_comment": "Confidence level for the forecast intervals.",
  "error_metric": "rmse",
  "_comment": "Metric to use for evaluating model performance. Options: 'rmse', 'mae', 'mse'.",
  "cross_validation": {
    "enabled": true,
    "_comment": "Enable or disable cross-validation.",
    "initial": "365 days",
    "_comment": "Amount of time to train the model initially. Example: '365 days', '6 months'.",
    "period": "90 days",
    "_comment": "Spacing between cutoff dates. Example: '90 days', '3 months'.",
    "horizon": "30 days",
    "_comment": "Size of the forecast horizon. Example: '30 days', '1 month'."
  },
  "feature_engineering": {
    "add_lags": false,
    "_comment": "Add lagged values of the time series as features.",
    "num_lags": 7,
    "_comment": "Number of lags to add if add_lags is true.",
    "add_seasonal_dummies": false,
    "_comment": "Add seasonal dummy variables.",
    "add_time_trend": false
    "_comment": "Add a time trend feature."
  },
  "anomaly_detection": {
    "enabled": false,
    "_comment": "Enable or disable anomaly detection.",
    "threshold": 0.95
    "_comment": "Threshold for anomaly detection."
  },
  "output_format": "json",
  "_comment": "Format of the forecast output. Options: 'json', 'csv'.",
  "dataset_name": "example_sales_data",
  "_comment": "Name of the dataset for identification purposes."
}