This three part write up [Part II Part III] is my attempt at a down-to-earth explanation (and Python code) of the Holt-Winters method for those of us who while hypothetically might be quite good at math, still try to avoid it at every opportunity. I had to dive into this subject while tinkering on tgres (which features a Golang implementation). And having found it somewhat complex (and yet so brilliantly simple), figured that it'd be good to share this knowledge, and in the process, to hopefully solidify it in my head as well. Triple Exponential Smoothing, also known as the Holt-Winters method, is one of the many methods or algorithms that can be used to forecast data points in a series, provided that the series is "seasonal", i.e. repetitive over some period. In 1957 an MIT and University of Chicago graduate, professor Charles C Holt (1921-2010) was working at CMU (then known as CIT) on forecasting trends in production, inventories and labor force.