Processing time series data: What are the options?

Processing time series data: What are the options?

Get your data from everywhere you can, anytime you can, they said, so you did. Now, you have a series of data points through time (a time series) in your hands, and you don’t know what to do with it? Worry not, because there’s a bunch of options.

Google does not always get things right, or get to things first. But when Google sets its sights on something, you know that something is about to attract interest. With Google having just announced its Cloud Inference API to uncover insights from time series data, it’s a good time to check the options for processing time series data.

A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

That’s how Wikipedia defines time series, and by that definition, most data starts looking like time series. That’s why time series data processing is important, and will become even more important going forward: If you keep recording values for the same thing, time after time, what you have is a time series.

Read the full article on ZDNet

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