Renoise multiplicative combining series#
When I first started doing time series analysis, the only way to visualise how a time series splits into different components was to use base R. You can have a time series that is somewhere in between the two, a statistician’s “it depends”, but I’m interested in attaining a quick classification so I won’t be handling this complication here. This is often seen in indexed time series where the absolute value is growing but changes stay relative. If you have an increasing trend, you still see roughly the same size peaks and troughs throughout the time series. In an additive time series, the components add together to make the time series. This is common when you’re looking at web traffic. If you have an increasing trend, the amplitude of seasonal activity increases. In a multiplicative time series, the components multiply together to make the time series.
![renoise multiplicative combining renoise multiplicative combining](https://rekkerd.org/img/200905/renoise_2_1.jpg)
How these three components interact determines the difference between a multiplicative and an additive time series. – error/ residual/ irregular activity not explained by the trend or the seasonal value – seasonality how things change within a given period e.g. There are three components to a time series: It’s important to understand what the difference between a multiplicative time series and an additive one before we go any further. This post looks at how we can classify a given time series as one or the other to facilitate further processing.
![renoise multiplicative combining renoise multiplicative combining](https://i.ytimg.com/vi/LBGHBlbLcXE/maxresdefault.jpg)
![renoise multiplicative combining renoise multiplicative combining](https://i.pinimg.com/originals/51/6d/90/516d90a8089413b202c33f3558c48c5a.jpg)
The interactions between trend and seasonality are typically classified as either additive or multiplicative. To be able to analyse time series effectively, it helps to understand the interaction between general seasonality in activity and the underlying trend. Time series data is an important area of analysis, especially if you do a lot of web analytics.