tigerforecast.utils package

dataset_registry

unemployment([verbose])	Description: Checks if unemployment data exists, downloads if not.
uci_indoor([verbose])	Description: Checks if uci_indoor data exists, downloads if not.
sp500([verbose])	Description: Checks if S&P500 data exists, downloads if not.
crypto([verbose])	Description: Checks if cryptocurrency data exists, downloads if not.
enso(input_signals, include_month, …)	Description: Transforms the ctrl_indices dataset into a format suitable for online learning.

random

set_key([key])	Descripton: Fix global random key to ensure reproducibility of results.
generate_key()	Descripton: Generate random key.
get_global_key()	Descripton: Get current global random key.

optimizers

optimizers.Optimizer([pred, loss, …])	Description: Core class for method optimizers
optimizers.Adagrad([pred, loss, …])	Description: Ordinary Gradient Descent optimizer.
optimizers.Adam([pred, loss, learning_rate, …])	Description: Ordinary Gradient Descent optimizer.
optimizers.ONS([pred, loss, learning_rate, …])	Online newton step algorithm.
optimizers.SGD([pred, loss, learning_rate, …])	Description: Stochastic Gradient Descent optimizer.
optimizers.OGD([pred, loss, learning_rate, …])	Description: Ordinary Gradient Descent optimizer.
optimizers.mse(y_pred, y_true)	Description: mean-square-error loss :param y_pred: value predicted by method :param y_true: ground truth value :param eps: some scalar
optimizers.cross_entropy(y_pred, y_true[, eps])	Description: cross entropy loss, y_pred is equivalent to logits and y_true to labels :param y_pred: value predicted by method :param y_true: ground truth value :param eps: some scalar

boosting

boosting.SimpleBoost()

Description: Implements the equivalent of an AR(p) method - predicts a linear combination of the previous p observed values in a time-series

autotuning

autotuning.grid_search.GridSearch()

Description: Implements the equivalent of an AR(p) method - predicts a linear combination of the previous p observed values in a time-series