Visualizing a time series is an essential step in exploring its behavior. Statisticians think of a time series as a combination of four components: trend, seasonality, level and noise. All real-world series contain a level and noise, but not necessarily a trend and/or seasonality. It is important to determine whether trend and/or seasonality exist in a series in order to choose appropriate models and methods for descriptive or forecasting purposes. Hence, looking at a time plot,  typical questions include: is there a trend? if so, what type of function can approximate it? (linear, exponential, etc.) is the trend fixed throughout the period … Continue reading Visualizing time series: suppressing one pattern to enhance another pattern