FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models

Deep learning-based weather forecasting models have recently demonstrated significant performance improvements over gold-standard physics-based simulation tools. However, these models are vulnerable to adversarial attacks, which raises concerns about their trustworthiness. In this paper, we first investigate the feasibility of applying existing adversarial attack methods to weather forecasting models. We argue that a successful attack should (1) not modify significantly its original inputs, (2) be faithful, i.e., achieve the desired forecast at targeted locations with minimal changes to non-targeted locations, and (3) be geospatio-temporally realistic. However, balancing these criteria is a challenge as existing methods are not designed to preserve the geospatio-temporal dependencies of the original samples. To address this challenge, we propose a novel framework called FABLE (Forecast Alteration By Localized targeted advErsarial attack), which employs a 3D discrete wavelet decomposition to extract the varying components of the geospatio-temporal data. By regulating the magnitude of adversarial perturbations across different components, FABLE can generate adversarial inputs that maintain geospatio-temporal coherence while remaining faithful and closely aligned with the original inputs. Experimental results on multiple real-world datasets demonstrate the effectiveness of our framework over baseline methods across various metrics.

On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense

Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient. It is important to evaluate how vulnerable different time series models are to attacks and how well they recover using defense. The sensitivity to various attacks and the robustness using the defense of several time series models are investigated in this study. Experiments are run on seven-time series models with three adversarial attacks and one adversarial defense. According to the findings, all models, particularly GRU and RNN, appear to be vulnerable. LSTM and GRU also have better defense recovery. FGSM exceeds the competitors in terms of attacks. PGD attacks are more difficult to recover from than other sorts of attacks.

DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data

Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.

The Influences of Pre-birth Factors in Early Assessment of Child Mortality using Machine Learning Techniques

Analysis of child mortality is crucial as it pertains to the policy and programs of a country. The early assessment of patterns and trends in causes of child mortality help decision-makers assess needs, prioritize interventions, and monitor progress. Post-birth factors of the child, such as real-time clinical data, health data of the child, etc. are frequently used in child mortality studies. However, in the early assessment of child mortality, pre-birth factors would be more practical and beneficial than the post-birth factors. This study aims at incorporating pre-birth factors, such as birth history, maternal history, reproduction history, socioeconomic condition, etc. for classifying child mortality. To assess the relative importance of the features, Information Gain (IG) attribute evaluator is employed. For classifying child mortality, four machine learning algorithms are evaluated. Results show that the proposed approach achieved an AUC score of 0.947 in classifying child mortality which outperformed the clinical standards. In terms of accuracy, precision, recall, and f-1 score, the results are also notable and uniform. In developing countries like Bangladesh, the early assessment of child mortality using pre-birth factors would be effective and feasible as it avoids the uncertainty of the post-birth factors.