EAT

EAT (ETS, auto.arima, Theta) model

[source]

EAT

ahead.EAT.EAT.EAT(
    h=5,
    level=95,
    weights=[0.3333333333333333, 0.3333333333333333, 0.3333333333333333],
    type_pi="E",
    date_formatting="original",
)

Combinations of ETS (exponential smoothing), auto.arima and Theta models

Parameters:

h: an integer;
    forecasting horizon

level: an integer;
    Confidence level for prediction intervals

weights: a list;
    coefficients assigned to each method in the ensemble

type_pi: a string;
    Type of prediction interval (currently "gaussian",
    ETS: "E", Arima: "A" or Theta: "T")

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

Attributes:

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

averages_: a list;
    mean forecast in a list

ranges_: a list;
    lower and upper prediction intervals in a list

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_df_: a data frame;
    contains 3 columns, mean forecast, lower + upper
    prediction intervals, and a date index

Examples:

import pandas as pd
from ahead import EAT

# Data frame containing the time series
dataset = {
'date' : ['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01'],
'value' : [34, 30, 35.6, 33.3, 38.1]}

df = pd.DataFrame(dataset).set_index('date')
print(df)

# univariate time series forecasting
e1 = EAT(h = 5) # default, equal weights for each model=[1/3, 1/3, 1/3]
e1.forecast(df)
print(e1.result_df_)

---

[source]

forecast

EAT.forecast(df)

Forecasting method from EAT class

Parameters:

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

---