Large amount of time series data generated by sensors and Web users is great source of contextual information. Detecting outliers with unusually high values in time series data is crucial for inferring about any events in the real world. In this work, we describe an infinite Poisson mixture model to detect events by identifying outliers in time series of count data. This unsupervised technique estimates the probability densities of count data which have an unknown Poisson mixture while it simultaneously detects outliers in the data. The advantage of our model is that outliers are mapped to mixture components discovered by infinite mixture model and thus inference can be drawn on the different 'types' of outliers and their proportions in the data. This lets us identify and categorize events based on magnitude of outlier data. We have analysed the performance of our model against a well known event detection technique based on Markov Modulated Poisson Process (MMPP) using synthetic and real world data. Results show that our approach to detecting events is more appropriate in analysing periodic count data as compared to the MMPP baseline. The experiments demonstrate that the presented model provides robust, detailed, and interpretable results for the analysis of outliers to detect events.