Classical Model Research Articles

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Data Augmentation for Sample Efficient and Robust Document Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this article, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

On null models for temporal small-worldness in brain dynamics

Abstract Brain dynamics can be modeled as a temporal brain network starting from the activity of different brain regions in functional magnetic resonance imaging (fMRI) signals. When validating hypotheses about temporal networks, it is important to use an appropriate statistical null model that shares some features with the treated empirical data. The purpose of this work is to contribute to the theory of temporal null models for brain networks by introducing the random temporal hyperbolic (RTH) graph model, an extension of the random hyperbolic (RH) graph, known in the study of complex networks for its ability to reproduce crucial properties of real-world networks. We focus on temporal small-worldness which, in the static case, has been extensively studied in real-world complex networks and has been linked to the ability of brain networks to efficiently exchange information. We compare the RTH graph model with standard null models for temporal networks and show it is the null model that best reproduces the small-worldness of resting brain activity. This ability to reproduce fundamental features of real brain networks, while adding only a single parameter compared with classical models, suggests that the RTH graph model is a promising tool for validating hypotheses about temporal brain networks.

A Quantum Evolutionary Learning Tracker for Video

Video object tracking has been a popular area in the field of computer vision. As video data evolves, more special perspectives and challenging video data are constantly kept up to date. This poses challenges for object tracking tasks and places higher demands on the generalization capabilities of the models. In this paper, we propose a novel quantum evolutionary learning tracker for video. The model combines quantum evolution with deep networks for tracking video objects. The model uses a quantum evolutionary learning tracker to generate a reliable population of candidate regions and a deep network for classification. In particular, the quantum evolutionary predictor predicts the object motion state through rotation operator and trajectory inference, and provides motion state information for the tracker. The predictor can incorporate object history contextual information and can provide stable candidate estimation populations for the model in case of failure of appearance features. Both quantum evolution and deep networks are combined to form an end-to-end online video object tracker. In addition, we propose a new video object tracking evaluation algorithm, Balanced Intersection over Union. The evaluation algorithm uses aspect ratios to balance the share of overlap and distance. Finally, we test the model on the OTB 2015 dataset for natural video and on the SV248A10-SOT dataset for satellite video. The performance of the proposed model is also analyzed and validated by comparing it with more than twenty classical tracker models. The experimental results show that our model has high generalization ability and robustness.

The Dilemma of Quantum Neural Networks.

The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bounds than their classical counterparts to ensure better reliability and interpretability. Recent studies confirmed that quantum neural networks (QNNs) have the ability to achieve this goal on specific datasets. In this regard, it is of great importance to understand whether these advantages are still preserved on real-world tasks. Through systematic numerical experiments, we empirically observe that current QNNs fail to provide any benefit over classical learning models. Concretely, our results deliver two key messages. First, QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. Second, the trainability of QNNs is insensitive to regularization techniques, which sharply contrasts with the classical scenario. These empirical results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.

Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models.

We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.

Evolutionary Dual-Stream Transformer.

Vision transformers (ViTs) are rapidly evolving and are widely used in computer vision. However, high-performance ViTs require many computations, which limit their further development in the vision field. In this article, a novel evolutionary dual-stream transformer (E-DST) model is proposed to alleviate the computational resource demand problem. A hybrid attention mechanism structure is proposed for a DST model. The DST model uses a dual-branch structure to fuse convolutional and transformer features. Combining the features learned by the transformer and convolution effectively saves model computational resources. In addition, an evolutionary optimizer is proposed to optimize the parameters of the model. The excellent search ability of the evolutionary algorithm is utilized to optimize the transformer model parameters. The convergence of the evolutionary optimizer is proved in this article. In addition, the proposed E-DST model is experimentally compared with a variety of classic models and their deformations based on three datasets. And, the evolutionary optimizer proves its generality in convolutional and recurrent neural networks. The experimental results show that the E-DST model can effectively reduce computational resources and that the evolutionary optimizer can solve large-scale optimization problems. In conclusion, our proposed method is feasible and effective.

A node-based SIRS epidemic model on two-layer interconnected networks: Dynamical analysis of interplay between layers

From the micro-level perspective of individuals, this paper proposes a node-based SIRS epidemic model on two-layer networks, and investigates the stability of the equilibrium in the model as well as the influence of network structures and infected or recovered rate on disease propagation. By using the stability theory, we give the accurate sufficient condition for the stability as well as the threshold of disease prevalence, showing the threshold of such two-layer network is less than that of isolated single-layer network. Also, an easy-to-use approximate criterion for stability of disease-free equilibrium is provided for the two-layer network with general intra-layer structures when the infected rate across layers is far less than that within layers. Further, extensive numerical simulations on several classical network structure models verify the theoretical results, and show some new interesting results: (1) for the duplex network with equivalent number of connections each layer, the infected rate across layers has more significant effect on epidemic spreading than that within layers; (2) when fixing the whole network edge density, the same number of connections each layer always be the most conductive to propagate of the disease for the duplex network of ER structures, but it is the most/worst conductive to propagate at large/small infected rate for the duplex network of small-world structures.

Mack’s estimator motivated by large exposure asymptotics in a compound poisson setting

AbstractThe distribution-free chain ladder of Mack justified the use of the chain ladder predictor and enabled Mack to derive an estimator of conditional mean squared error of prediction for the chain ladder predictor. Classical insurance loss models, that is of compound Poisson type, are not consistent with Mack’s distribution-free chain ladder. However, for a sequence of compound Poisson loss models indexed by exposure (e.g., number of contracts), we show that the chain ladder predictor and Mack’s estimator of conditional mean squared error of prediction can be derived by considering large exposure asymptotics. Hence, quantifying chain ladder prediction uncertainty can be done with Mack’s estimator without relying on the validity of the model assumptions of the distribution-free chain ladder.

Unravelling Expressive Delegations: Complexity and Normative Analysis

We consider binary group decision-making under a rich model of liquid democracy: agents submit ranked delegation options, where each option may be a function of multiple agents' votes; e.g., "I vote yes if a majority of my friends vote yes." Such ballots are unravelled into a profile of direct votes by selecting one entry from each ballot so as not to introduce cyclic dependencies. We study delegation via monotonic Boolean functions, and two unravelling procedures: MinSum, which minimises the sum of the ranks of the chosen entries, and its egalitarian counterpart, MinMax. We provide complete computational dichotomies: MinSum is hard to compute (and approximate) as soon as any non-trivial functions are permitted, and polynomial otherwise; for MinMax the easiness results extend to arbitrary-arity logical ORs and ANDs taken in isolation, but not beyond. For the classic model of delegating to individual agents, we give asymptotically near-tight algorithms for carrying out the two procedures and efficient algorithms for finding optimal unravellings with the highest vote count for a given alternative. These algorithms inspire novel tie-breaking rules for the setup of voting to change a status quo. We then introduce a new axiom, which can be viewed as a variant of the participation axiom, and use algorithmic techniques developed earlier in the paper to show that it is satisfied by MinSum and a lexicographic refinement of MinMax (but not MinMax itself).

Cancer Risk Identification using Machine Learning

In this comprehensive study, we explore the SEER (Surveillance, Epidemiology, and End Result) dataset to meticulously craft a predictive model aimed at understanding patient survivability, focusing keenly on outcomes like patient survival diagnosis and treatments. Employing a good approach involving rigorous data cleaning, intricate feature extraction, and detailed correlation analysis, we identify the dominant attributes that significantly influence patient outcomes. Our methodology integrates classical machine learning models, including decision trees and random forests, with modern techniques such as neural networks. This mix of methodologies allows us to accurately predict patient survivability. Furthermore, our study delves into the intricate relationships between attributes, models, and algorithms, aiming to identify the dominant factors that influence the outcomes. Our groundbreaking findings underscore the enormous potential of integrating these dominant attributes, paving the way for the creation of exceptionally robust predictive models. These models will substantially enhance medical decision-making processes and, in turn, elevate the overall quality of patient care. Keywords - SEER dataset, patient survivability, machine learning, decision trees, random forests, neural networks, data cleaning, feature extraction, clinical data, medical decision-making.

Effects of flow speed and prey density on the rate and efficiency of prey capture in zooplanktivorous coral-reef fishes

Holling’s classical functional response model describes the mechanistic foundations of the relationships between predation rate and prey density. As such, the model is pertinent to predators that actively search for prey, but not to stationary predators in which additional factors, such as flow speed, determine the rates of prey encounter. The main objective of this study was to measure the rates and corresponding efficiencies of zooplanktivory among different common species of coral-reef fishes under a wide range of prey densities and current speeds. All our experiments were carried out in a flume with different combinations of flow speeds (3-28.5 cm/s) and prey densities (210 - 1050 prey m-3). Nauplii of Artemia salina were used as prey. Despite major differences in the taxonomic origin of the studied species, their morphologies, and the types of shelters they use, the foraging performances of the fish, their predation rates, and the way those rates were affected by prey density and flow speed were surprisingly similar. Under a fixed prey density, capture rates did not change much as function of flow speed. Under conditions of equal prey flux, predation rates were always higher under conditions of high density and weaker flow than under lower density and faster flow. A sharp decline in capture efficiency with increasing flow speed was explained by a corresponding narrowing of the fish’s body orientation relative to the flow. In other words, with increasing flow speed, the fish gradually became more-narrowly oriented head-on onto the flow, exhibiting a decrease in the frequency of body turns (“maneuverability”). These trends, especially the reduced maneuverability under strong currents, can explain our findings that predation rates did not increase when the flow, hence prey flux, increased. Inter-specific differences in predation rates and efficiencies, however small, agree well with observed differences in the type of habitats the different species occupy.

Impact of endogenous analgesia triggered by acupuncture, stress, or noxious stimulation on REM sleep-deprived rats.

Poor sleep increases pain, at least in part, by disrupting endogenous pain modulation. However, the efficacy of endogenous analgesia in sleep-deprived subjects has never been tested. To assess this issue, we chose three different ways of triggering endogenous analgesia: (1) acupuncture, (2) acute stress, and (3) noxious stimulation, and compared their ability to decrease the pronociceptive effect induced by REM-SD (Rapid Eye Movement Sleep Deprivation) with that to decrease inflammatory hyperalgesia in the classical carrageenan model. First, we tested the ability of REM-SD to worsen carrageenan-induced hyperalgesia: Alow dose of carrageenan (30 µg) in sleep-deprived Wistar rats resulted in a potentiated hyperalgesic effectthat was more intense and longer-lasting than that induced by a higher standard dose of carrageenan (100 µg) or by REM-SD alone. Then, we found that (1) acupuncture, performed at ST36, completely reversed the pronociceptive effect induced by REM-SD or by carrageenan; (2) immobilization stress completely reversed the pronociceptive effect of REM-SD, while transiently inhibited carrageenan-induced hyperalgesia; (3) noxious stimulation of the forepaw by capsaicin also reversed the pronociceptive effect of REM-SD and persistently increased the nociceptive threshold above the baseline in carrageenan-treated animals. Therefore, acupuncture, stress, or noxious stimulation reversed the pronociceptive effect of REM-SD, while each intervention affected carrageenan-induced hyperalgesia differently. This study has shown that while sleep loss may disrupt endogenous pain modulation mechanisms, it does not prevent theactivation of these mechanisms to induce analgesia in sleep-deprived individuals.

Unlocking maintenance insights in industrial text through semantic search

Maintenance records in Computerized Maintenance Management Systems (CMMS) contain valuable human knowledge on maintenance activities. These records primarily consist of noisy and unstructured texts written by maintenance experts. The technical nature of the text, combined with a concise writing style and frequent use of abbreviations, makes it difficult to be processed through classical Natural Language Processing (NLP) pipelines. Due to these complexities, this text must be normalized before feeding to classical machine learning models. Developing these custom normalization pipelines requires manual labor and domain expertise and is a time-consuming process that demands constant updates. This leads to the under-utilization of this valuable source of information to generate insights to help with maintenance decision support. This study proposes a Technical Language Processing (TLP) pipeline for semantic search in industrial text using BERT (Bidirectional Encoder Representations), a transformer-based Large Language Model (LLM). The proposed pipeline can automatically process complex unstructured industrial text and does not require custom preprocessing. To adapt the BERT model for the target domain, three unsupervised domain fine-tuning techniques are compared to identify the best strategy for leveraging available tacit knowledge in industrial text. The proposed approach is validated on two industrial maintenance records from the mining and aviation domains. Semantic search results are analyzed from a quantitative and qualitative perspective. Analysis shows that TSDAE, a state-of-the-art unsupervised domain fine-tuning technique, can efficiently identify intricate patterns in the industrial text regardless of associated complexities. BERT model fine-tuned with TSDAE on industrial text achieved a precision of 0.94 and 0.97 for mining excavators and aviation maintenance records, respectively.

Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis

Rolling bearings being important components of mechanical equipment, the accurate fault diagnosis method of rolling bearings is of great importance to ensure production safety. Permutation entropy is a nonlinear measure of the irregularity of time series, which involves calculating permutation patterns, that is, defining permutations by comparing adjacent values of the time series. When using graph signal processing technology to analyze the vibration signal of rolling bearing, the natural visibility graph (NVG) can better reflect the dynamic characteristics of the vibration signal than path graph (PG). In this paper, the multiscale permutation entropy (MPE) is defined on NVG, and it is used to characterize the different fault characteristics of rolling bearings. The sand cat swarm optimization (SCSO) algorithm is employed to optimize the parameters of support vector machine (SVM); The MPEs of different faults of rolling bearing which defined on NVG are regarded as the fault feature set input into optimized SVM, and it is applied to characterize the different fault characteristics of rolling bearings, realizing fault diagnosis of rolling bearing. The proposed method is used to analyze the experimental data which contain both normal and faulty rolling bearings. The experiment results show that the proposed method can diagnose the bearing faults effectively. The MPE based on NVG is superior to MPE based on PG and MPE based on the vibration signal in distinguishing the different damage states of rolling bearings. The classification accuracy of optimized SVM based on SCSO algorithm is higher than other classical models. The effectiveness and feasibility of defining entropy on the graph signal and as the fault feature vectors for rolling bearing to realize fault diagnosis is validated. The results indicate that the proposed method can effectively detect bearing faults, and demonstrate its effectiveness and robustness for rolling bearing fault diagnosis.

Modeling of Some Classes of Extended Oscillators: Simulations, Algorithms, Generating Chaos, and Open Problems

In this article, we propose some extended oscillator models. Various experiments are performed. The models are studied using the Melnikov approach. We show some integral units for researching the behavior of these hypothetical oscillators. These will be implemented as add-on sections of a thoughtful main web-based application for researching computations. One of the main goals of the study is to share the difficulties that researchers (who are not necessarily professional mathematicians) encounter in using contemporary computer algebraic systems (CASs) for scientific research to examine in detail the dynamics of modifications of classical and newer models that are emerging in the literature (for the large values of the parameters of the models). The present article is a natural continuation of the research in the direction that has been indicated and discussed in our previous investigations. One possible application that the Melnikov function may find in the modeling of a radiating antenna diagram is also discussed. Some probability-based constructions are also presented. We hope that some of these notes will be reflected in upcoming registered rectifications of the CAS. The aim of studying the design realization (scheme, manufacture, output, etc.) of the explored differential models can be viewed as not yet being met.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Special Relativity in Terms of Hyperbolic Functions with Coupled Parameters in 3+1 Dimensions

This paper presents a method for parameterizing new Lorentz spacetime coordinates based on coupled parameters. The role of symmetry in rapidity in special relativity is explored, and invariance is obtained for new spacetime intervals with respect to the Lorentz transformation. Using the Euler–Hamilton equations, an additional angular rapidity and perpendicular rapidity are obtained, and the Hamiltonian and Lagrangian of a relativistic particle are expanded into rapidity spectra. A so-called passage to the limit is introduced that makes it possible to decompose physical quantities into spectra in terms of elementary functions when explicit decomposition is difficult. New rapidity-dependent Lorentz spacetime coordinates are obtained. The descriptions of particle motion using the old and new Lorentz spacetime coordinates as applied to plane laser pulses are compared in terms of the particle kinetic energy. Based on a classical model of particle motion in the field of a plane monochromatic electromagnetic wave and that of a plane laser pulse, rapidity-dependent spectral decompositions into elementary functions are presented, and the Euler–Hamilton equations are derived as rapidity functions in 3+1 dimensions. The new and old Lorentz spacetime coordinates are compared with the Fermi spacetime coordinates. The proper Lorentz groups SO(1,3) with coupled parameters using the old and new Lorentz spacetime coordinates are also compared. As a special case, the application of Lorentz spacetime coordinates to a relativistic hydrodynamic system with coupled parameters in 1+1 dimensions is demonstrated.

Bayesian Forecasting of Mortality Rates for Small Areas Using Spatiotemporal Models.

Estimation and prediction of subnational mortality rates for small areas are essential planning tools for studying health inequalities. Standard methods do not perform well when data are noisy, a typical behavior of subnational datasets. Thus, reliable estimates are difficult to obtain. I present a Bayesian hierarchical model framework for prediction of mortality rates at a small or subnational level. By combining ideas from demography and epidemiology, the classical mortality modeling framework is extended to include an additional spatial component capturing regional heterogeneity. Information is pooled across neighboring regions and smoothed over time and age. To make predictions more robust and address the issue of model selection, a Bayesian version of stacking is considered using leave-future-out validation. I apply this method to forecast mortality rates for 96 regions in Bavaria, Germany, disaggregated by age and sex. Uncertainty surrounding the forecasts is provided in terms of prediction intervals. Using posterior predictive checks, I show that the models capture the essential features and are suitable to forecast the data at hand. On held-out data, my predictions outperform those of standard models lacking a regional component.