Identifying times when water is unsafe for recreation, for drinking, or for aquatic life is a major challenge. Traditionally, sampling has been the preferred means of determining whether water is safe. Predictive modeling based on artificial intelligence (AI) is an approach that is becoming more and more popular.
A time series is a set of repeated measurements taken sequentially over time. The main purpose of time series analysis is to predict the future of a certain process based on what has happened in the past.
Before any project, it is crucial to understand the difference between the following data types: numerical, categorical, continuous, discrete, nominal and ordinal. This knowledge is key to fully grasp the statistical nature of the available data and to properly handle any given features. Despite its simplicity, this step is essential to achieve a robust and meaningful data analysis. In fact, data types usually dictate which imputation strategies, statistical measurements, plot designs and algorithms are the most appropriate to use.