predict_trend¶

predict_trend(fit_result: Dict[str, Union[lmfit.model.ModelResult, pandas.core.frame.DataFrame]], days_to_predict: int = 30, func_options: dict = {}, param_inverted_stderr: Iterable[str] = [], brute_force_extrema: bool = False) → pandas.core.frame.DataFrame[source]¶

Generic function to predict a trend from fitted data

Parameters

fit_result (Dict[str, Union[lmfit.model.ModelResult, pd.DataFrame]]) – result of fit_data_model or its implementation
days_to_predict (int, optional) – number of days to predict a trend for, by default 30
func_options (dict, optional) – options for the function of model, by default {}
param_inverted_stderr (Iterable[str], optional) – iterable of parameternames with should be inverted, to calculate the extrema. , by default []
brute_force_extrema (bool, optional) – Whether or not to calculate supremum and infimum from all permutations of adding and subtracting the errors from the parameters. For some functions, i.e. the logistic curve, this is needed, since simply adding or subtracting the errors from the parameter can lead to supremum and/or infimum to cross the result with the exact parameters., by default False

Returns

DataFrame with columns “date”, “trend”, “trend_sup” and “trend_inf”

date: pd.Datetime: date of the values
trend: float: predicted trend
trend_sup: float: supremum of the trend
trend_inf: float: infimum of the trend

Return type

pd.DataFrame