African Drought Risk Pay-Out Benchmarking

This report contains exploratory data analysis of rainfall and Water Resource Sufficiency Index (WRSI) data provided by African Risk Ca- pacity (ARC). The purpose is to assess the predictability of droughts in Africa. We assess the appropriateness of the historical WRSI bench- marks set by ARC members compared to the observed WRSI values for different regions. We conclude that the benchmarks are broadly sensible. We then compare a number of linear time series models based on their ability to fit and forecast the WRSI time series. We conclude that sim- pler models like Simple Moving Average and Moving Median are more appropriate than more sophisticated models containing trends and sea- sonality like Holt Winter and TBATS. We also investigate the use of the SARIMA and TBATS models to forecast the seasonal patterns observed in rainfall data and conclude that both models can generate structured forecasts that reflect seasonal variability. The statistical evidence how- ever favoured TBATS over SARIMA. Attempts to measure the influence of the El Nin ̃o-Southern Oscillation on rainfall levels are inconclusive for the areas studied. Finally we perform a simple application of univariate Extreme Value Theory to rainfall data and conclude that further inves- tigation is necessary to understand how the catastrophic famine that affected Ethiopia in the early 1980’s would be reflected in the data if a similar event were to reoccur today

Strategies for the use of Data and Algorithmic Approaches in Railway Traffic Management

A Railway Traffic Management problem can be defined as forecasting fu- ture progression of trains, identifying conflicts where two or more trains compete for available infrastructure, investigating options for resolution of conflicts, re-planning train schedules to minimise the impact on sy- stem performance. Performance management of complex networks is a problem common to a number of industries and applications. There has been much work over many decades on modelling the generation and optimisation of railway timetables. Much of this focuses on relatively simple railways and services and is therefore quite straightforward. Main line railways have a number of features that introduce significant com- plexity. Traditionally the problem of re-planning a timetable in near real time to manage and recover from service perturbations and disruption is simplified to help arrive at a solution in an acceptable amount of time, but this then can have unintended consequences which can amplify rat- her than reduce the disruption in the network. Resonate are interested in looking at different strategies / models / techniques for dealing with the problem, the likely strengths and risks of these, and how they might be adapted to improve existing solutions. The study group participants undertook a brief survey of recent literature on modelling train delays and found machine learning approaches, network models and a statisti- cal approach to defining the efficiency of a station in dissipating delays which are worthy of further consideration. We then explored total of nine modelling approaches during the study group. The approaches fell broadly into two groups: those that sought to understand the pro- pagation of delays (Approaches 1 to 6) and those that sought to offer strategies for minimising delays (Approaches 8 and 9). Approach 7 pro- poses a way of understanding the propagation of delays and using that to evaluate candidate policy decisions. There are a number of promising approaches here which provide useful lines of enquiry, many suitable for expansion beyond the simple railways modelled, to include variable train speeds, junctions and intersections, temporal differences in usage, such as tidal flows in and out of cities, and resource constraints