Remote sensing plays a vital role in climate change studies by providing us with a record of 'snapshots' of the Earth's environment through time. Almost 30 years have past since the launch of the first routine and repetitive remote sensing systems. We are on the brink of being able to use remotely sensed imagery to define climatic normals using features of the Earth's surface environment as proxy indicators. Before we can do this, however, we need to develop new image analysis strategies—hypertemporal image analysis techniques—which we can use to measure variability and identify changes in long sequences of remotely sensed imagery. In this paper we explore one such technique: statistical time series analysis (TSA). We apply AutoRegressive-Moving Average (ARMA) time series models to a 108-month sequence of passive microwave images of Arctic sea ice concentrations. TSA is traditionally a manual technique applied to one or two temporal variables. Through this research we demonstrate how, by making two simple assumptions about the data, the approach can be automated to process the thousands of time series in a temporal image sequence. ARMA models are shown to be useful in deriving a spatial summary of the temporal characteristics of the modelled phenomenon, for identifying changes within that system, and for forecasting into the future with statistical confidence.