Trends and Opportunities in Time Series - Gradient Flow

While univariate models for representing time series are useful in many applications, jointly modeling multiple time series can increase accuracy of various tasks, such as forecasting, anomaly detection and pattern discovery. There are two main hindrances to applying these models more widely. The first is wide variability of behaviors of different time series, making it harder to capture their different behaviors in a single model. Solutions may be borrowed from other challenges ML researchers face in other data domains: for example, in other machine learning tasks, different scales between features is addressed using normalization techniques. A second challenge is joint modeling of time series that are measured/reported at different time intervals (e.g., reporting every second, minute or hour), and sometimes reported at irregular intervals.