Incorporating time series analysis into your business

Time series analysis is as vast a topic as time itself, and as such, there is far too much information to cover in one article. This article is the first part in a series to go over the many intricacies of time series analysis. Read part two, Production deployment of time series forecasting, to learn more.

Modern growth in computational processes impacts big data and artificial intelligence (AI) which directly benefit time series analysis. With more data, increasingly efficient computation, and greater processing power capabilities; leveraging time series methods to solve business problems through forecasting and process modeling has increased. Through these improvements, business owners have a unique opportunity to harness their data through new and old time series analysis processes to better anticipate future customer insights and market trends while making business decisions.

What are time series?

A time series is data measured in chronological order. The intervals of measurement can be regular or irregular, and can vary in length. For example, time series can be monthly, weekly, daily, or even to the second. Much of the data we collect are time series, such as stock prices, machine telemetry, and temperature readings over an established timeframe. Additionally, granular data can be converted to a time series; for example, individual clicks on a website can be aggregated to measure daily website activity. Time series analysis includes several techniques and models designed for this kind of data. The growth of big data has created increasing opportunities to leverage time series analyses.

What is time series analysis?

Time series analysis is a set of methods that allow us to understand time series data. This can be as simple as understanding when seasonal trends occur, but usually involves deeper analysis to build a more complete model of the underlying processes. Building and implementing a time series model describes a fundamental correlation within the data.  Being able to identify these correlations generates insight into the data that can be leveraged to forecast and predict future values.

Why use time series analysis?

Time series analysis is especially useful for forecasting. In forecasting, time series methods often perform better than simpler methods, such as linear regression or simple extrapolation of historical data.