Source code for tigercontrol.models.time_series.last_value

"""
Last observed value
"""

import jax.numpy as np
import tigercontrol
from tigercontrol.models.time_series import TimeSeriesModel

[docs]class LastValue(TimeSeriesModel): """ Description: Predicts the last value in the time series, i.e. x(t) = x(t-1) """ compatibles = set(['TimeSeries'])
[docs] def __init__(self): self.initialized = False self.uses_regressors = False
def initialize(self): """ Description: Initialize the (non-existent) hidden dynamics of the model Args: None Returns: None """ self.initialized = True def predict(self, x): """ Description: Takes input observation and returns next prediction value Args: x (float/numpy.ndarray): value at current time-step Returns: Predicted value for the next time-step """ return x def forecast(self, x, timeline = 1): """ Description: Forecast values 'timeline' timesteps in the future Args: x (int/numpy.ndarray): Value at current time-step timeline (int): timeline for forecast Returns: Forecasted values 'timeline' timesteps in the future """ return np.ones(timeline) * x def update(self, y): """ Description: Takes update rule and adjusts internal parameters Args: y (float/np.ndarray): true value Returns: None """ return def help(self): """ Description: Prints information about this class and its methods Args: None Returns: None """ print(LastValue_help) def __str__(self): return "<LastValue Model>"
# string to print when calling help() method LastValue_help = """ -------------------- *** -------------------- Id: LastValue Description: Predicts the last value in the time series, i.e. x(t) = x(t-1) Methods: initialize() Description: Initialize the (non-existent) hidden dynamics of the model Args: None Returns: None step(x) Description: Takes input observation and returns next prediction value, then updates internal parameters Args: x (float/numpy.ndarray): value at current time-step Returns: Predicted value for the next time-step predict(x) Description: Takes input observation and returns next prediction value Args: x (float/numpy.ndarray): value at current time-step Returns: Predicted value for the next time-step update(rule=None) Description: Takes update rule and adjusts internal parameters Args: rule (function): rule with which to alter parameters Returns: None help() Description: Prints information about this class and its methods Args: None Returns: None -------------------- *** -------------------- """