A new Python version of ahead
, v0.9.0
is now available on GitHub and PyPI.
ahead
is a Python and R package for univariate and multivariate time series forecasting, with uncertainty
quantification (in particular, simulationbased uncertainty quantification).
Here are the new features in v0.9.0
:

progress bars for possibly long calculations: the bootstrap (independent, circular block, moving block)

plot for
Ridge2Regressor
(a work in progress, still needs to use series names, and display dates correctly, for all classes, not justRidge2Regressor
)
Since this implementation is based on the R version, it could take some time to import R packages when using Python’s ahead
for the first time. There’s something new regarding this situation (well… ha ha): R packages are now installed on the fly. Meaning: only when they’re required.
Example 1
Start by installing ahead
v0.9.0:
pip install ahead
import numpy as np
import pandas as pd
from time import time
from ahead import Ridge2Regressor # this is where the R packages are installed (if not available in the environment, and ONLY the 1st time)
url = "https://raw.githubusercontent.com/thierrymoudiki/mtsdata/master/heatericecream/ice_cream_vs_heater.csv"
df = pd.read_csv(url)
df.set_index('Month', inplace=True) # only for ice_cream_vs_heater
df.index.rename('date') # only for ice_cream_vs_heater
df = df.pct_change().dropna()
regr1 = Ridge2Regressor(h = 10, date_formatting = "original",
type_pi="rvinecopula",
margins="empirical",
B=50, seed=1)
regr1.forecast(df) # this is where the R packages are installed (if not available in the environment, and ONLY the 1st time)
regr1.plot(0) # dates are missing, + want to use series names
regr1.plot(1)
Example 2
regr2 = Ridge2Regressor(h = 10, date_formatting = "original",
type_pi="movingblockbootstrap",
B=50, seed=1)
regr2.forecast(df) # a progress bar is displayed
regr2.plot(0) # dates are missing, + want to use series names
regr2.plot(1)