Getting Started with Time Series Data Science

Are you interested in performing time series forecasting or anomaly detection, but you don’t know where to start? If so, you’re not alone. There is an overwhelming variety of libraries, algorithms, and workflow recommendations for these tasks. As a Developer Advocate at InfluxDB, the leading time series database, I’ve researched time series data science methodologies and best practices for forecasting and anomaly detection. Today I want to summarize some important concepts about time series as well as share resources to get you started on your time series data science journey.

Why should a beginner interested in data science start learning about time series?

If you’re interested in becoming a data scientist, learning about data science as it pertains to time series is a great place to start. Time series data is data that is indexed chronologically. Because it’s indexed in time, often times, each time series data point is related to what came before. To explain what I mean, let’s take a look at weather data. The temperature of the city you live in right now is correlated to the temperature an hour ago and even last week or the same time last year. In other words, the temperature data is correlated with itself at other points in time. This statistical phenomenon is called autocorrelation, and it is one of the reasons that time series data is unique in the data world.