def __init__( self, name: str = "TSFreshRegressor", frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, regression_model: str = 'Adaboost', max_timeshift: int = 10, feature_selection: str = None, ): ModelObject.__init__( self, name, frequency, prediction_interval, regression_type=regression_type, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose, ) self.regression_model = regression_model self.max_timeshift = max_timeshift self.feature_selection = feature_selection
def __init__( self, name: str = "TensorflowSTS", frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, trend: str = 'local', seasonal_periods: int = None, ar_order: int = None, fit_method: str = 'hmc', num_steps: int = 200, ): ModelObject.__init__( self, name, frequency, prediction_interval, regression_type=regression_type, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose, ) self.seasonal_periods = seasonal_periods self.ar_order = ar_order self.trend = trend self.fit_method = fit_method self.num_steps = num_steps
def __init__( self, name: str = "TFPRegression", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 1, kernel_initializer: str = 'lecun_uniform', optimizer: str = 'adam', loss: str = 'negloglike', epochs: int = 50, batch_size: int = 32, dist: str = 'normal', regression_type: str = None, ): ModelObject.__init__( self, name, frequency, prediction_interval, regression_type=regression_type, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose, ) self.verbose = verbose self.random_seed = random_seed self.kernel_initializer = kernel_initializer self.epochs = int(epochs) self.batch_size = int(batch_size) self.optimizer = optimizer self.loss = loss # negloglike, Huber, mae self.dist = dist # normal, poisson, negbinom
def __init__(self, name: str = "MotifSimulation", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, phrase_len: str = '5', comparison: str = 'magnitude_pct_change_sign', shared: bool = False, distance_metric: str = 'l2', max_motifs: float = 50, recency_weighting: float = 0.1, cutoff_threshold: float = 0.9, cutoff_minimum: int = 20, point_method: str = 'median', verbose: int = 1, **kwargs): ModelObject.__init__( self, name, frequency, prediction_interval, holiday_country=holiday_country, random_seed=random_seed, ) self.phrase_len = phrase_len self.comparison = comparison self.shared = shared self.distance_metric = distance_metric self.max_motifs = max_motifs self.recency_weighting = recency_weighting self.cutoff_threshold = cutoff_threshold self.cutoff_minimum = cutoff_minimum self.point_method = point_method
def __init__(self, name: str = "SeasonalNaive", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, lag_1: int = 7, lag_2: int = None, method: str = 'LastValue', **kwargs): ModelObject.__init__( self, name, frequency, prediction_interval, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose, ) self.lag_1 = abs(int(lag_1)) self.lag_2 = lag_2 if str(self.lag_2).isdigit(): self.lag_2 = abs(int(self.lag_2)) if str(self.lag_2) == str(self.lag_1): self.lag_2 = 1 self.method = method
def __init__( self, name: str = "GluonTS", frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, gluon_model: str = 'DeepAR', epochs: int = 20, learning_rate: float = 0.001, context_length=10, forecast_length: int = 14, ): ModelObject.__init__( self, name, frequency, prediction_interval, regression_type=regression_type, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose, ) self.gluon_model = gluon_model if self.gluon_model == 'NPTS': self.epochs = 20 self.learning_rate = 0.001 else: self.epochs = epochs self.learning_rate = learning_rate self.context_length = context_length self.forecast_length = forecast_length
def __init__(self, name: str = "LastValueNaive", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020): ModelObject.__init__(self, name, frequency, prediction_interval, holiday_country=holiday_country, random_seed=random_seed)
def __init__(self, name: str = "ZeroesNaive", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0): ModelObject.__init__(self, name, frequency, prediction_interval, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose)
def __init__(self, name: str = "AverageValueNaive", frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, method: str = 'Median'): ModelObject.__init__(self, name, frequency, prediction_interval, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose) self.method = method
def __init__(self, name: str = "FBProphet", frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0): ModelObject.__init__(self, name, frequency, prediction_interval, regression_type=regression_type, holiday_country=holiday_country, random_seed=random_seed, verbose=verbose) self.holiday = holiday