Holt Winters Forecasting Model in R скачать в хорошем качестве

Holt Winters Forecasting Model in R

See my best offer for my course on TIme Series Forecasting in R: https://www.udemy.com/course/forecast... 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. Triple exponential smoothing applies exponential smoothing three times, which is commonly used when there are three high-frequency signals to be removed from a time series under study. There are different types of seasonality: 'multiplicative' and 'additive' in nature, much like addition and multiplication are basic operations in mathematics. If every month of December we sell 10,000 more apartments than we do in November the seasonality is additive in nature. However, if we sell 10% more apartments in the summer months than we do in the winter months the seasonality is multiplicative in nature. Multiplicative seasonality can be represented as a constant factor, not an absolute amount. Triple exponential smoothing was first suggested by Holt's student, Peter Winters, in 1960 after reading a signal processing book from the 1940s on exponential smoothing. Holt's novel idea was to repeat filtering an odd number of times greater than 1 and less than 5, which was popular with scholars of previous eras. While recursive filtering had been used previously, it was applied twice and four times to coincide with the Hadamard conjecture, while triple application required more than double the operations of singular convolution. The use of a triple application is considered a rule of thumb technique, rather than one based on theoretical foundations, and has often been over-emphasized by practitioners. Intro (0:00) Data pre-processing (0:40) Simple Exponential Smoothing (3:18) Outro (4:28)