Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
A few weeks ago, I introduced the R version of ahead, a package for univariate and multivariate time series forecasting. A Python version, built on top of the R version, is now available on PyPI and GitHub. Here is how to install it:
-
1st method: from PyPI (stable version)
pip install ahead -
2nd method: from Github (development version)
pip install git+https://github.com/Techtonique/ahead_python.git
Here are the packages that will be used for this demo:
import pandas as pd # for creating Python time series data structures
import ahead as ah # might take some time installing R packages, ONLY the 1st time it's called
Univariate time series forecasting
# Input time series
dataset = {
'date' : ['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01',
'2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01'],
'value' : [34, 30, 35.6, 33.3, 38.1, 39.2, 37.3, 34.5, 35.6, 35.9]}
# Data frame containing the time series
df = pd.DataFrame(dataset).set_index('date')
# For more details on EAT class parameters, visit
# https://techtonique.github.io/ahead_python/documentation/eat/
e1 = ah.EAT(h = 5)
e1.forecast(df)
print(e1.result_df_)
mean lower upper
2020-11-01 35.995003 30.538903 41.451110
2020-12-01 36.059549 30.603449 41.515655
2021-01-01 36.124094 30.667994 41.580201
2021-02-01 36.188640 30.732540 41.644746
2021-03-01 36.253185 30.797085 41.709292
Multivariate time series forecasting
# Input 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]}
# Data frame containing the (3) time series
df = pd.DataFrame(dataset).set_index('date')
# For more details on Ridge2Regressor class parameters, visit
# https://techtonique.github.io/ahead_python/documentation/ridge2regressor/
r1 = ah.Ridge2Regressor(h = 5)
r1.forecast(df)
print(r1.result_dfs_[0])
print(r1.result_dfs_[1])
print(r1.result_dfs_[2])
mean lower upper
2006-01-01 33.995386 33.927846 34.062925
2007-01-01 36.801638 36.734099 36.869178
2008-01-01 33.080255 33.012716 33.147795
2009-01-01 36.101237 36.033697 36.168777
2010-01-01 33.584086 33.516547 33.651626
mean lower upper
2006-01-01 5.488425 5.474439 5.502412
2007-01-01 4.891770 4.877784 4.905756
2008-01-01 5.536466 5.522480 5.550452
2009-01-01 5.187249 5.173263 5.201236
2010-01-01 5.680144 5.666158 5.694130
mean lower upper
2006-01-01 99.936592 99.929847 99.943337
2007-01-01 100.110577 100.103832 100.117322
2008-01-01 100.097996 100.091251 100.104741
2009-01-01 100.235343 100.228598 100.242089
2010-01-01 100.136356 100.129611 100.143101
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