BasicForecaster

Basic forecasting functions for multivariate time series (mean, median, random walk)

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BasicForecaster

ahead.Basic.BasicForecaster.BasicForecaster(
    h=5, level=95, method="mean", type_pi="gaussian", B=100, date_formatting="original", seed=123
)

Basic forecasting functions for multivariate time series (mean, median, random walk)

Parameters:

h: an integer;
    forecasting horizon

level: an integer;
    Confidence level for prediction intervals

method: a string;
    Forecasting method, either "mean", "median", or random walk ("rw")

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

B: an integer;
    Number of bootstrap replications 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 BasicForecaster

# 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 = BasicForecaster(h = 5)
r1.forecast(df)
print(r1.result_dfs_)

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[source]

forecast

BasicForecaster.forecast(df)

Forecasting method from BasicForecaster class

Parameters:

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

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