Forecasting with `ahead` (Python version)

Posted on December 13, 2021 by T. Moudiki in Data science | 0 Comments

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A few weeks ago, I introduced the R version of

ahead

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)
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pip install ahead
pip install ahead
```
  pip install ahead
```

2nd method: from Github (development version)

pip install git+https://github.com/Techtonique/ahead_python.git

  pip install git+https://github.com/Techtonique/ahead_python.git

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_)

# 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

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])

# 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

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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Forecasting with `ahead` (Python version)

Univariate time series forecasting

Multivariate time series forecasting

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