This is a work in progress
augur /ˈȯ-gər/
NOUN
1. (in ancient Rome) a religious official who observed and interpreted
omens and signs to help guide the making of public decisions
2. any prophet or soothsayer
VERB
3. to predict (some future event), as from signs or omens
tsaugur is a low-code Python package for time series forecasting. It allows data scientists to fit, tune, compare, evaluate, ensemble and visualize a range of models quickly and with just a few lines of code.
tsaugur is basically a unified interface to a number of Python libraries for time series prediction, such as prophet, statsmodels, tbats, pmdarima, pydlm and more, plus some ensembling and plotting features.
Load example data and split into train and test set:
from tsaugur.datasets import load_dataset
data = load_dataset("air_passengers")
y_train = data["passengers"][:134]
y_test = data["passengers"][134:]Check available models:
from tsaugur.models import print_available_models
print_available_models()Model Description Exogeneous variables
--------------------------------------------------------------------------------------------
holt_winters Holt-Winters Exponential Smoothing no
sarima Seasonal Auto-Regression Integrated Moving Average yes
fourier_sarima Auto-Regression Integrated Moving Average yes
with seasonality captured by Fourier terms
tbats Trigonometric seasonality, Box-Cox transformation, no
ARMA errors, Trend and Seasonal components
bdlm Bayesian Dynamic Linear Model yes
prophet Facebook's Prophet model yes
Fit and tune a Holt-Winters Exponential Smoothing model:
from tsaugur.models import create_model
model = create_model("holt_winters")
model.fit(y_train, period=12)Make and plot predictions for the next 24 periods:
preds = model.predict(horizon=24)
model.plot_predict(horizon=24)Score and plot the model against the test set:
score = model.score(y_test, metric="smape")
model.plot_score(y_test, metric="smape")Tabulate error metrics for different models:
from tsaugur.models import compare_models
comp = compare_models(["holt_winters", "sarima", "tbats"],
y_train, y_test, period=12)
comp.tabulate() smape mae mse rmse
sarima 2.715588 12.837482 249.713469 15.802325
holt_winters 3.864753 18.928886 523.328546 22.876375
tbats 5.129382 25.555115 883.309555 29.720524
Plot model comparison:
comp.plot()in progress
This project is authored and maintained by Michał Oleszak.
MIT License
© 2020 Michał Oleszak
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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