Ridge2Regressor

Random Vector functional link network model with 2 regularization parameters

[source]

Ridge2Regressor

ahead.Ridge2.Ridge2Regressor.Ridge2Regressor(
    h=5,
    level=95,
    lags=1,
    nb_hidden=5,
    nodes_sim="sobol",
    activation="relu",
    a=0.01,
    lambda_1=0.1,
    lambda_2=0.1,
    dropout=0,
    type_pi="gaussian",
    B=100,
    cl=1,
    date_formatting="original",
    seed=123,
)

Random Vector functional link network model with 2 regularization parameters

Parameters:

h: an integer;
    forecasting horizon

level: an integer;
    Confidence level for prediction intervals

lags: an integer;
    Number of lags

nb_hidden: an integer;
    Number of nodes in hidden layer

nodes_sim: an integer;
    Type of simulation for nodes in the hidden layer
    ("sobol", "halton", "unif")

activation: a string;
    Activation function ("relu", "sigmoid", "tanh",
    "leakyrelu", "elu", "linear")

a: a float;
    hyperparameter for activation function "leakyrelu", "elu"

lambda_1: a float;
    Regularization parameter for original predictors

lambda_2: a float;
    Regularization parameter for transformed predictors

dropout: a float;
    dropout regularization parameter (dropping nodes in hidden layer)

type_pi: a string;
    Type of prediction interval (currently "gaussian",
    or "bootstrap")

B: an integer;
    Number of bootstrap replications for `type_pi == bootstrap`

cl: an integer; 
    The number of clusters for parallel execution (done in R), for `type_pi == bootstrap`

date_formatting: a string;
    Currently:
    - "original": yyyy-mm-dd
    - "ms": milliseconds

seed: an integer;
    reproducibility seed for type_pi == 'bootstrap'

Attributes:

fcast_: an object;
    raw result from fitting R's `ahead::ridge2f` through `rpy2`

averages_: a list of lists;
    mean forecast in a list for each series

ranges_: a list of lists;
    lower and upper prediction intervals in a list for each series

output_dates_: a list;
    a list of output dates (associated to forecast)

mean_: a numpy array
    contains series mean forecast as a numpy array

lower_: a numpy array 
    contains series lower bound forecast as a numpy array

upper_: a numpy array 
    contains series upper bound forecast as a numpy array

result_dfs_: a tuple of data frames;
    each element of the tuple contains 3 columns,
    mean forecast, lower + upper prediction intervals,
    and a date index

sims_: currently a tuple of numpy arrays
    for `type_pi == bootstrap`, simulations for each series

Examples:

import pandas as pd
from ahead import Ridge2Regressor

# Data frame containing the time series
dataset = {
'date' : ['2001-01-01', '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01'],
'series1' : [34, 30, 35.6, 33.3, 38.1],
'series2' : [4, 5.5, 5.6, 6.3, 5.1],
'series3' : [100, 100.5, 100.6, 100.2, 100.1]}
df = pd.DataFrame(dataset).set_index('date')
print(df)

# multivariate time series forecasting
r1 = Ridge2Regressor(h = 5)
r1.forecast(df)
print(r1.result_dfs_)

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[source]

forecast

Ridge2Regressor.forecast(df)

Forecasting method from Ridge2Regressor class

Parameters:

df: a data frame;
    a data frame containing the input time series (see example)

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