(News from) forecasting in Python with ahead (progress bars and plots)
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A new Python version of aheadahead,
v0.9.0
v0.9.0 is now available on GitHub and PyPI.ahead
ahead is a Python and R package for univariate and multivariate time series forecasting, with uncertaintyquantification (in particular, simulation-based uncertainty quantification).
Here are the new features in
v0.9.0
v0.9.0:progress bars for possibly long calculations: the bootstrap (independent, circular block, moving block)
plot for
Ridge2RegressorRidge2Regressor(a work in progress, still needs to use series names, and display dates correctly, for all classes, not justRidge2RegressorRidge2Regressor)
Since this implementation is based on the R version, it could take some time to import R packages when using Python’s
ahead
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
import numpy as np
import pandas as pd
from time import time
url = "https://raw.githubusercontent.com/thierrymoudiki/mts-data/master/heater-ice-cream/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()
import numpy as np
import pandas as pd
from time import time
url = "https://raw.githubusercontent.com/thierrymoudiki/mts-data/master/heater-ice-cream/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()
import numpy as np
import pandas as pd
from time import time
url = "https://raw.githubusercontent.com/thierrymoudiki/mts-data/master/heater-ice-cream/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)
regr1.plot(0) # dates are missing, + want to use series names
regr1.plot(1)
regr1 = Ridge2Regressor(h = 10, date_formatting = "original",
type_pi="rvinecopula",
margins="empirical",
B=50, seed=1)
regr1.forecast(df)
regr1.plot(0) # dates are missing, + want to use series names
regr1.plot(1)
regr1 = Ridge2Regressor(h = 10, date_formatting = "original",
type_pi="rvinecopula",
margins="empirical",
B=50, seed=1)
regr1.forecast(df)
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)
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)
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)
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