Presentation given at UNSW
It is becoming increasingly common for organizations to collect very large amounts of data over time. Data visualization is essential for exploring and understanding structures and patterns, and to identify unusual observations. However, the sheer quantity of data available means that new time series visualisation methods are needed. I will demonstrate an approach to this problem using a vector of features on each time series, measuring characteristics of the series. These feature vectors can then be mapped to a 2-dimensional space for visualization. The feature-based approach to time series can also be used for many other analysis tasks including (1) clustering time series; (2) identifying anomalous time series within a collection of time series; (3) selecting the best forecasting model; and (4) finding the optimal weighted ensemble of forecasts. I will demonstrate examples for each of these, and show some new R packages that make feature-based time series analysis easy to do.