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 = "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 = "Greykite",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday: bool = False,
growth: str = None,
regression_type: str = None,
holiday_country: str = 'US',
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
self.holiday = holiday
self.growth = growth
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 = "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 = "LastValueNaive",
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
random_seed: int = 2020,
**kwargs):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
)
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',
yearly_seasonality="auto",
weekly_seasonality="auto",
daily_seasonality="auto",
growth: str = "linear",
n_changepoints: int = 25,
changepoint_prior_scale: float = 0.05,
seasonality_mode: str = "additive",
changepoint_range: float = 0.8,
seasonality_prior_scale: float = 10.0,
holidays_prior_scale: float = 10.0,
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
self.holiday = holiday
self.regressor_name = []
self.yearly_seasonality = yearly_seasonality
self.weekly_seasonality = weekly_seasonality
self.daily_seasonality = daily_seasonality
self.growth = growth
self.n_changepoints = n_changepoints
self.changepoint_prior_scale = changepoint_prior_scale
self.seasonality_mode = seasonality_mode
self.changepoint_range = changepoint_range
self.seasonality_prior_scale = seasonality_prior_scale
self.holidays_prior_scale = holidays_prior_scale
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',
**kwargs):
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 = "NeuralProphet",
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,
n_jobs: int = None,
growth: str = "off",
n_changepoints: int = 10,
changepoints_range: float = 0.9,
trend_reg: float = 0,
trend_reg_threshold: bool = False,
ar_sparsity: float = None,
yearly_seasonality: str = "auto",
weekly_seasonality: str = "auto",
daily_seasonality: str = "auto",
seasonality_mode: str = "additive",
seasonality_reg: float = 0,
n_lags: int = 0,
num_hidden_layers: int = 0,
d_hidden: int = None,
learning_rate: float = None,
loss_func: str = "Huber",
train_speed: int = None,
normalize: str = "auto",
):
ModelObject.__init__(
self,
name,
frequency,
prediction_interval,
regression_type=regression_type,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
self.holiday = holiday
self.regressor_name = []
self.growth = growth
self.n_changepoints = n_changepoints
self.changepoints_range = changepoints_range
self.trend_reg = trend_reg
self.trend_reg_threshold = trend_reg_threshold
self.ar_sparsity = ar_sparsity
self.yearly_seasonality = yearly_seasonality
self.weekly_seasonality = weekly_seasonality
self.daily_seasonality = daily_seasonality
self.seasonality_mode = seasonality_mode
self.seasonality_reg = seasonality_reg
self.n_lags = n_lags
self.num_hidden_layers = num_hidden_layers
self.d_hidden = d_hidden
self.learning_rate = learning_rate
self.loss_func = loss_func
self.train_speed = train_speed
self.normalize = normalize