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