Time Series Data Types

Introduction

A key part of data analytics involves working with data collected over time. A time series is a set of data points collected over time, and these data points can represent anything from stock prices and weather measurements to social media metrics and website traffic.

The main feature of a time series is that the data points are ordered chronologically, with each data point corresponding to a specific point in time. The ability to analyze time series data is important for a number of reasons:

• Trend analysis: Time series analysis allows us to identify trends and patterns in the data that may not be apparent from a simple visualization of the raw data.

• Forecasting: Time series analysis allows us to predict future behavior based on historical data. For example, we can use time series analysis to forecast demand for a particular product.

• Seasonality: Many time series exhibit seasonal patterns, such as increased sales during the summer months. With this understanding, we can better allocate resources for future events.

• Anomaly detection: Time series analysis can help us identify unusual or anomalous behaviors. For example, we can use time series analysis to identify unusual patterns in financial transactions.

The pandas library has an extensive range of capabilities and features to work with time series data, and it’s something we’ll explore in greater detail in the following lessons. ...