We present a novel Bayesian approach to analysing multiple time-series with the aim of detecting abnormal regions. These are regions where the properties of the data change from some normal or baseline behaviour. We allow for the possibility that such changes will only be present in a, potentially small, subset of the time-series. We develop a general model for this problem, and show how it is possible to accurately and efficiently perform Bayesian inference, based upon recursions that enable independent sampling from the posterior distribution. A motivating application for this problem comes from detecting copy number variation (CNVs), using data from multiple individuals. Pooling information across individuals can increase the power of detecting CNVs, but often a specific CNV will only be present in a small subset of the individuals. We evaluate the Bayesian method on both simulated and real CNV data, and give evidence that this approach is more accurate than a recently proposed method for analysing such data.

Detecting recent changepoints in time-series can be important for short-term prediction, as we can then base predictions just on the data since the changepoint. In many applications we have panel data, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such panel data which aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computationally efficient as it involves analysing each time-series independently to obtain a profile-likelihood like quantity that summarises the evidence for the series having either no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series which has their most recent changepoint at that time. We demonstrate the usefulness of this method on two data sets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms.

Existing literature on inter-rater reliability focuses on quantifying the disagreement between raters. In this paper, we introduce a method to correct for inter-rater disagreement (or observer bias), where raters are assigning scores on a continuous scale. To do this, we propose a two-stage approach. In the first stage, we standardise the distributions of rater scores to account for each rater's subjective interpretation of the continuous scale. In the second stage, we correct for case-mix differences between raters by exploiting pairwise information where two raters have read the same entity on a case. We illustrate the use of our procedure on clinicians' visual assessments of breast density (a risk factor for breast cancer). After applying our procedure, 229 out of 1398 women who were originally classified as high density were re-classified as non-high density, and 382 out of 12 348 women were re-classified from non-high to high density. A simulation study also demonstrates good performance of the proposed method over a range of scenarios.

The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting.The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method.

We propose an intent-based system where, on top of the user intentions, the system itself generates suitable Quality of Service and resilience parameters and may augment the intent characteristics if it detects any room for improvement. We demonstrate the feasibility and challenges of such a system using mininet and the ONOS controller.