Abstract: Uncertainty is a major and important property in the study of time series data mining under the background of multi-granular research. It is found that the instability of temporal granularity itself is a widespread phenomenon, but due to the variability of granularity, it is a non-trivial task to develop feasible solutions. As a result, few papers report efficient idea for this problem. To describe it systematically, a basic model of the uncertain multi-granularity time series with related basic definition is proposed initially. Based on the above theory, the reason of uncertain time series granularity is analyzed, and a strategy of stabling time granularity algorithm is then proposed. Finally, an optimal strategy of granularity selection for multi-granularity time series and a tactic for decreasing the variability of uncertain granularity are studied and discussed respectively. Uncertain multi-granularity time series data clustering requires cluster analysis since it is a fundamental job for further process such as prediction and analysis in time-series data, but due to the complexity, it becomes a special challenging. We introduce a stable temporal granularity clustering algorithm framework to solve the uncertain time series data clustering problem. Finally, an Intensive Care Unit Survival prediction experiment with the property of uncertainty in temporal granularity is conducted to verify the above theory. Results show that different choices of time granularity produces different outcome which is in line with the computational law of granular computing, and stable strategy for granularity can achieve satisfied prediction.