Irregularly observed time series can be tricky to analyze, but researchers have come up with a way to handle them. They created models based on autoregression that work well for both stationary and non-stationary time series. These models can compute basic statistics and make predictions accurately. The researchers also found a method to turn irregular time series into regular ones, making it easier to use existing analysis techniques.