Understanding Time Series with R - KDnuggets

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Analyzing time series can be an extremely useful resource for virtually any business, therefore, it is extremely important for data scientists entering the field to have a solid foundation on the main concepts. Luckily, a time series can be decomposed into its different elements and the decomposition of the process allows us to understand each of the parts that play a role in the analysis as a logical component of the whole system. While it is true that R has already many powerful packages to analyze time series, in this article the goal is to perform a time series analysis --specifically for forecasting-- by building a function from scratch to analyze each of the different elements in the process. There are several methods to do forecasting, but in this article, we'll focus on the multiplicative time series approach, in which we have the following elements: Seasonality: It consists of variations that occur at regular intervals, for example, every quarter, on summer vacation, at the end of the year, etc. A clear example would be higher conversion rates on gym memberships in January or a spike in video game sales around the holidays.

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