  Recurrent major mood episodes and subsyndromal mood instability cause
substantial disability in patients with bipolar disorder. Early identification
of mood episodes enabling timely mood stabilisation is an important clinical
goal. Recent technological advances allow the prospective reporting of mood in
real time enabling more accurate, efficient data capture. The complex nature of
these data streams in combination with challenge of deriving meaning from
missing data mean pose a significant analytic challenge. The signature method
is derived from stochastic analysis and has the ability to capture important
properties of complex ordered time series data. To explore whether the onset of
episodes of mania and depression can be identified using self-reported mood
data.
