The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed
time series data has been difficult to obtain until recently. However some papers have addressed the subject and
claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data
collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is
investigated using surrogate data techniques and nonlinear forecasting. For the surrogate data analysis, forecast error
and time reversal asymmetry statistics are used. The original time series cannot be distinguished from their linear
surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear
forecastingmethods. Nonlinear sample forecastingmethods have no advantage over linear methods in out-of-sample
forecasting for time series sampled on a weekly basis. These results can mean that either the original series have linear
dynamics, the test statistics for distinguishing linear from nonlinear behaviour do not have the power to detect the
kind of nonlinearity present, or the process is nonlinear but the sampling is inadequate to represent the dynamics. We
suggest that further studies should apply similar techniques to more frequently sampled data.
Keywords: Bipolar disorder; Mood dynamics; Time series analysis; Public healthcare