VAR

Vector AutoRegressive model

[source]

VAR

ahead.VAR.VAR.VAR(h=5, level=95, lags=1, type_VAR="none", date_formatting="original")

Vector AutoRegressive model

Parameters:

h: an integer;
    forecasting horizon

level: an integer;
    Confidence level for prediction intervals

lags: an integer;
    the lag order

type_VAR: a string;
    Type of deterministic regressors to include
    ("const", "trend", "both", "none")

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

Attributes:

fcast_: an object;
    raw result from fitting R's `ahead::varf` 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

Examples:

import pandas as pd
from ahead import VAR

# 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
v1 = VAR(h = 5, date_formatting = "original", type_VAR="none")
v1.forecast(df)
print(v1.result_dfs_)

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[source]

forecast

VAR.forecast(df)

Forecasting method from VAR class

Parameters:

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

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