From the course: Data Science Foundations: Data Mining in R

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Time-series mining overview

Time-series mining overview

From the course: Data Science Foundations: Data Mining in R

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Time-series mining overview

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- [Instructor] Perhaps you've seen on an old clock, the saying, "Tempus Fugit," Latin for, "Time flies." And that is that the crux of an important issue in analyzing temporal data. The idea is that it doesn't stay still, it's moving, and the problem is, it's only moving in one direction. Time only moves forward, and that gives you special analytical challenges. Fortunately, there are several methods, within analysis in general and data mining in particular, to deal with this temporal orientation of time-based data. The first one that we're going to look at, and the most basic, is called decomposition. It's like taking that phone apart to see what it's made of. With decomposition, or uncomposing, taking apart, you're going to separate time series data into several elements, a smooth trend, seasonal variation and noise, that's not adequately explained by those other two elements. Time series decomposition is generally a…

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