DynamicRegressor

Dynamic Regression model adapted from R's forecast::nnetar

[source]

DynamicRegressor

ahead.DynamicRegressor.DynamicRegressor.DynamicRegressor(
    h=5, level=95, type_pi="E", date_formatting="original"
)

Dynamic Regression Model adapted from R's forecast::nnetar

Parameters:

h: an integer;
    forecasting horizon

level: an integer;
    Confidence level for prediction intervals

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::dynrmf` 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 DynamicRegressor

# 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
d1 = DynamicRegressor(h = 5)
d1.forecast(df)
print(d1.result_df_)

---

[source]

forecast

DynamicRegressor.forecast(df)

Forecasting method from DynamicRegressor class

Parameters:

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

---