Time underlies many interesting human behaviors, and hence, it is important that AI-powered IoT systems know how to deal with time-dependent data. Time can be represented either explicitly, for example, capturing data at regular intervals where the time-stamp is also part of data, or implicitly, for example, in speech or written text. The methods that allow us to capture inherent patterns in time-dependent data is called time series modeling.
The data that is captured at regular intervals is a time series data, for example, stock price data is a time series data. Let's take a look at Apple stock price data; this data can be downloaded from the NASDAQ site (https://www.nasdaq.com/symbol/aapl/historical). Alternatively, ...
