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# References |
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## Theory & Practice |
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[Decomposition of Time Series](https://en.wikipedia.org/wiki/Decomposition_of_time_series) |
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[Forecasting Principles and Practice - Residuals](https://otexts.com/fpp2/residuals.html) |
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[Forecasting Principles and Practice - Time Series Components](https://otexts.com/fpp2/components.html) |
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[How to Decompose Time Series Data into Trend and Seasonality](https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/) |
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[NIST Engineering Statistics Handbook](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc443.htm) |
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[Secular variation](https://en.wikipedia.org/wiki/Secular_variation) |
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[statsmodels.tsa.seasonal.DecomposeResult](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.DecomposeResult.html#statsmodels.tsa.seasonal.DecomposeResult) |
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[Time Series with Python](https://www.datacamp.com/tracks/time-series-with-python) |
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## Data |
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[Time Series with Python](https://www.datacamp.com/tracks/time-series-with-python) |