Massey Documents by Type
Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294
Browse
18 results
Search Results
Item Global sensitivity analysis of models for volcanic ash forecasting(Elsevier B V, 2025-10-01) Scott E; Whitehead M; Mead S; Bebbington M; Procter JVolcanic ash is a widespread and destructive volcanic hazard. Timely and accurate forecasts for ash deposition and dispersal help mitigate the risks of volcanic hazards to society. Producing these forecasts requires numerous simulations with varying input parameters to encapsulate uncertainty and accurately capture the actual event to deliver a reliable forecast. However, exploring all possible combinations of input parameters is computationally infeasible in the lead up to an eruption. This research explores the input space of two volcanic ash transport and dispersion models, Tephra2, which is based on a simplified analytical solution, and Fall3D, which is a computational model based on more general assumptions, in the context of forecasting an unknown future eruption. We use the exemplar of Taranaki Mounga (Mount Taranaki), Aotearoa New Zealand, which has an estimated 30% to 50% chance of an explosive eruption in the next 50 years. We statistically determine how much each input parameter contributes to model output variance through a global sensitivity analysis via Sobol’ indices and the extended Fourier Amplitude Sensitivity Test (eFAST). Our findings show that grain size distribution, diffusion, plume shape, and plume duration (Fall3D only) have a substantial first-order impact on model output variance. In contrast, mass, particle density, and plume height have minimal impact in the first-order but become influential when considering parameter-parameter inter-relationships (total-order). The results not only enhance our understanding of model sensitivities but also point to improved efficiency in forecasting efforts.Item Forecasting patient flows with pandemic induced concept drift using explainable machine learning(BioMed Central Ltd, 2023-04-21) Susnjak T; Maddigan PAccurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done. The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.Item On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions(Elsevier Inc, 2023-11) Schaerer M; Plessis CD; Nguyen MHB; van Aert RCM; Tiokhin L; Lakens D; Clemente EG; Pfeiffer T; Dreber A; Johannesson M; Clark CJ; Uhlmann EL; Abraham AT; Adamus M; Akinci C; Alberti F; Alsharawy AM; Alzahawi S; Anseel F; Arndt F; Balkan B; Baskin E; Bearden CE; Benotsch EG; Bernritter S; Black SR; Bleidorn W; Boysen AP; Brienza JP; Brown M; Brown SEV; Brown JW; Buckley J; Buttliere B; Byrd N; Cígler H; Capitan T; Cherubini P; Chong SY; Ciftci EE; Conrad CD; Conway P; Costa E; Cox JA; Cox DJ; Cruz F; Dawson IGJ; Demiral EE; Derrick JL; Doshi S; Dunleavy DJ; Durham JD; Elbaek CT; Ellis DA; Ert E; Espinoza MP; Füllbrunn SC; Fath S; Furrer R; Fiala L; Fillon AA; Forsgren M; Fytraki AT; Galarza FB; Gandhi L; Garrison SM; Geraldes D; Ghasemi O; Gjoneska B; Gothilander J; Grühn D; Grieder M; Hafenbrädl S; Halkias G; Hancock R; Hantula DA; Harton HC; Hoffmann CP; Holzmeister F; Hoŕak F; Hosch A-K; Imada H; Ioannidis K; Jaeger B; Janas M; Janik B; Pratap KC R; Keel PK; Keeley JW; Keller L; Kenrick DT; Kiely KM; Knutsson M; Kovacheva A; Kovera MB; Krivoshchekov V; Krumrei-Mancuso EJ; Kulibert D; Lacko D; Lemay EPA preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.Item How Visual Design of Severe Weather Outlooks Can Affect Communication and Decision-Making(American Meteorological Society, 2023-10-16) Clive MAT; Doyle EEH; Potter SH; Noble C; Johnston DMMultiday severe weather outlooks can inform planning beyond the hour-to-day windows of warnings and watches. Outlooks can be complex to visualize, as they represent large-scale weather phenomena overlapping across several days at varying levels of uncertainty. Here, we present the results of a survey (n 5 417) that explores how visual varia-bles affect comprehension, inferences, and intended decision-making in a hypothetical scenario with the New Zealand MetService Severe Weather Outlook. We propose that visualization of the time window, forecast area, icons, and uncertainty can influence perceptions and decision-making based on four key findings. First, composite-style outlooks that depict multiple days of weather on one map can lead to biased perceptions of the forecast. When responding to questions about a day for which participants accurately reported there was no severe weather forecast, those who viewed a composite outlook reported higher likelihoods of severe weather occurring, higher levels of concern about travel, and higher likelihoods of changing plans compared to those who viewed outlooks that showed weather for each day on a separate map, suggesting that they perceived the forecast to underrepresent the likelihood of severe weather on that day. Second, presenting uncertainty in an extrinsic way (e.g., “low”) can lead to more accurate estimates of likelihood than intrinsic formats (e.g., hue variation). Third, shaded forecast areas may lead to higher levels of confidence in the forecast than outlined forecast areas. Fourth, inclusion of weather icons can improve comprehension in some conditions. The results demonstrate how visualization can affect decision-making about severe weather and support several evidence-based considerations for effective design of long-term forecasts.Item Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model(IEEE, 2021-06-08) Yousuf MU; Al-Bahadly I; Avci E; Do TDMarkov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.Item One Health: A new definition for a sustainable and healthy future(PLOS, 2022-06-23) One Health High-Level Expert Panel (OHHLEP); Adisasmito WB; Almuhairi S; Behravesh CB; Bilivogui P; Bukachi SA; Casas N; Cediel Becerra N; Charron DF; Chaudhary A; Ciacci Zanella JR; Cunningham AA; Dar O; Debnath N; Dungu B; Farag E; Gao GF; Hayman DTS; Khaitsa M; Koopmans MPG; Machalaba C; Mackenzie JS; Markotter W; Mettenleiter TC; Morand S; Smolenskiy V; Zhou L; Dvorin JDItem Predicting replicability—Analysis of survey and prediction market data from large-scale forecasting projects(Public Library of Science (PLoS), 2021-04-14) Gordon M; Viganola D; Dreber A; Johannesson M; Pfeiffer TThe reproducibility of published research has become an important topic in science policy. A number of large-scale replication projects have been conducted to gauge the overall reproducibility in specific academic fields. Here, we present an analysis of data from four studies which sought to forecast the outcomes of replication projects in the social and behavioural sciences, using human experts who participated in prediction markets and answered surveys. Because the number of findings replicated and predicted in each individual study was small, pooling the data offers an opportunity to evaluate hypotheses regarding the performance of prediction markets and surveys at a higher power. In total, peer beliefs were elicited for the replication outcomes of 103 published findings. We find there is information within the scientific community about the replicability of scientific findings, and that both surveys and prediction markets can be used to elicit and aggregate this information. Our results show prediction markets can determine the outcomes of direct replications with 73% accuracy (n = 103). Both the prediction market prices, and the average survey responses are correlated with outcomes (0.581 and 0.564 respectively, both p < .001). We also found a significant relationship between p-values of the original findings and replication outcomes. The dataset is made available through the R package “pooledmaRket” and can be used to further study community beliefs towards replications outcomes as elicited in the surveys and prediction markets.Item Decision markets implementations for human forecasters and multi-agent learning systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand(Massey University, 2023) Wang, WenlongMechanisms of collective decision-making are an increasingly important topic, given that relevant data and information are often distributed. Collective decision-making processes involve eliciting information from multiple agents, aggregating the information, and mapping the aggregated information to a decision. An obstacle to these processes is that information is often proprietary, held by self-interested agents, and sometimes even too sensitive to share. Decision markets are mechanisms for eliciting and aggregating such information into predictions for decision-making. A design for decision markets put forward by Chen, Kash, Ruberry, et al. uses prediction markets to elicit and aggregate predictions that are conditional to the available actions, and then uses a stochastic decision rule to determine, based on the aggregated forecasts, which action to select. The design is incentive-compatible and uses a decision scoring rule to evaluate and incentivise the self-interested agents for their forecasts. The first part of this thesis (Chapter 2) describes a framework for security-based decision markets that allows agents to make predictions by trading assets. Security-based decision markets are designed to be user-friendly for participants familiar with trading in stock markets. For prediction markets, such a framework is well studied. For decision markets, my results show there are important differences between scoring rule based and securities-based implementation. The second and third parts of this thesis (Chapters 3 and 4) investigate decision markets as mechanisms of collective decision-making for multi-agent learning problems, thus building a bridge between economic mechanisms and artificial intelligence. Chapter 3 provides a decision market based algorithm that allows a principal to train multiple autonomous agents with independent and identically distributed (iid) information to solve a contextual bandit problem. Simulation results demonstrate that the proposed multi-agent systems can achieve performance equivalent to a centralised counterpart without requiring direct access to the agents' iid information, which is necessary for the centralised counterpart. Chapter 4 describes a set of mechanisms that allow avoiding stochastic decision rules to select actions based on aggregated forecasts. This is important because committing to a stochastic (i.e., randomising) decision rule means that sometimes suboptimal decisions have to be taken. The mechanisms outlined in this chapter require agents to collectively predict a proxy instead of conditional outcomes. Simulations show that the performance is as good as a Bayesian model with access to all distributed information.Item Essays on individual stock returns predictability : a thesis presented in fulfillment of the requirement for the degree of Doctor of Philosophy in Finance at Massey University, Albany, New Zealand(Massey University, 2022) Zeng, HuiThis dissertation considers different aspects of individual stock predictability. The first essay shows that the previously documented predictability of macroeconomic and technical variables for market returns is also evident in individual stock returns. Technical variables generate better predictability on firms with high limits to arbitrage (small, illiquid, volatile firms), while macroeconomic variables better predict firms with low limits to arbitrage. Technical predictors show a stronger predictive power for high limits to arbitrage firms across the business cycle, whereas macroeconomic variables capture more predictive information for firms with low limits to arbitrage during recessions. The second essay shows that 14 widely documented technical indicators explain cross-sectional expected returns. The technical indicators have lower estimation errors than the three-factor Fama-French model and historical mean. The long-short portfolios based on cross-sectional estimated returns consistently generate substantial profits across the entire period. The well-known cross-sectional expected return determinants, including momentum, size, book-to-market, investment, and profitability, do not explain the explanatory power of technical indicators. Our findings suggest that technical indicators play an important role in determining the variation in cross-sectional expected returns in addition to the five-factor model. In the third essay, we use firm characteristics to estimate the enduring momentum probabilities for past winners (losers) to continue to be future winners (losers). The enduring momentum probability is significantly related to stock return persistence and explains cross-sectional expected returns. In addition, it contains different information from momentum signals. Combining the two pieces of information generates an enduring momentum strategy that produces a 2.19% return per month, almost doubling the momentum return. Factors that drive the price momentum strategy, such as seasonality, limit to arbitrage, and transaction costs, do not fully capture the performance of the enduring momentum strategy.Item Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand(Massey University, 2022) Yousuf, Muhammad UzairIn recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further.