MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.Comment: Accepted by the 2022 International Joint Conference on Neural Networks (IJCNN 2022

Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand

Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1) sample-based approaches, where each forecast is made independently, and (2) time series regression approaches, where some historical load and other feature information is used. When making a short-to-mid-term electricity demand forecast, some future information is available, such as the weather forecast and calendar variables. However, in existing forecasting models this future information is not fully incorporated. To overcome this limitation of existing approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting (MMMPF), a novel and general framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make probabilistic predictions. Experiments are performed on a real-world dataset for short-to-mid-term electricity demand forecasting for multiple regions and compared with various ML methods. They show that the proposed MMMPF framework outperforms not only sample-based methods but also existing time-series forecasting models with the exact same base models. Models trainded with MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future.Comment: Accepted by the 2023 IEEE Power & Energy Society General Meeting (PESGM). arXiv admin note: substantial text overlap with arXiv:2209.1441

Modeling the dynamics of HPV infection by both low-risk and high-risk types and the development of cervical cancer cells

The development of cervical cells from normal cells infected by human papillomavirus into invasive cancer cells can be modeled using population dynamics of the cells and free virus. Since the human papillomavirus can be separated into low-risk types and high-risk types, the healthy cells may be infected by either type or both types simultaneously. We develop three within-host models of HPV infection to describe the dynamics of HPV infection by low-risk types and high-risk types and the development of cervical cancer by keeping tracking of the cell population in different compartments. We begin with discussion about the basic model with the assumption of constant growth rate of the normal cells. The second model is developed to monitor the growth of normal cells following a logistic growth curve. We will then discuss in more depth about the second model that includes to include the vaccination program. For each model, we solve for the equilibria, providing insight into whether an infection will persist or not, then perform stability analysis of the equilibrium points. In particular, explicit expression of the basic reproduction number R0 will be extracted using the model parameters, which serves as a threshold for the establishment of infection within-host. The endemic status which changes with respect to R0 can then be determined by numerical simulation. By sensitivity analysis, results of the parameters will show crucial correspondence to the progression of invasive cancer. Changes in precancerous cell population is affected by taking different parameter values, which helps to further explore their effect on the risk of cancer progression.California State University, Northridge. Department of Mathematics

Institutional investors, non-mandatory regulations, and board gender diversity

This study investigates the interaction between institutional investors and non-mandatory regulations, specifically, their impact on board gender diversity. Using a sample of UK FTSE All-Share firms from 2000 to 2017, we find that higher institutional ownership leads to higher female director representation on boards. We also find that this effect is more pronounced after the Davies intervention, a campaign promoting gender balance on British corporate boards. The findings highlight the complementary role of institutional investors and the Davies intervention in shaping board gender diversity, thereby offering insightful implications for shareholder perspectives and demand for board diversity