| # 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) |