# References
## Theory & Practice
[Decomposition of Time Series](https://en.wikipedia.org/wiki/Decomposition_of_time_series)

[Forecasting Principles and Practice - Residuals](https://otexts.com/fpp2/residuals.html)

[Forecasting Principles and Practice - Time Series Components](https://otexts.com/fpp2/components.html)

[How to Decompose Time Series Data into Trend and Seasonality](https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/)

[NIST Engineering Statistics Handbook](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc443.htm)

[Secular variation](https://en.wikipedia.org/wiki/Secular_variation)

[statsmodels.tsa.seasonal.DecomposeResult](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.DecomposeResult.html#statsmodels.tsa.seasonal.DecomposeResult)

[Time Series with Python](https://www.datacamp.com/tracks/time-series-with-python)

## Data
[Time Series with Python](https://www.datacamp.com/tracks/time-series-with-python)