Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (CONET), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-CONET framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable CONET learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available.Comment: Accepted by AAAI 202

Introduction to Biomedical Optical Imaging Issue

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149560/1/lsm23100_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149560/2/lsm23100.pd

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations

Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolutions. To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations. Specifically, we first develop a Dual-Transformer architecture, including a Spatial Transformer and a Temporal Transformer. The Spatial Transformer automatically learns the implicit spatial correlations across the road segments beyond the boundary of geographical structures, while the Temporal Transformer aims to capture the dynamic changing patterns of the implicit spatial correlations. Then, to further integrate both explicit and implicit spatial correlations, we propose a distillation-style learning framework, in which the existing traffic speed prediction methods are considered as the teacher model, and the proposed Dual-Transformer architectures are considered as the student model. The extensive experiments over three real-world datasets indicate significant improvements of our proposed framework over the existing methods

Natural gas utilization in China: Development trends and prospects

It has been an important objective in China’s energy development strategy to accelerate the promotion of natural gas utilization, cultivate main sources of natural gas, and increase the proportion of natural gas consumption. Based on the analysis of the development of natural gas utilization in China over the past 3 decades by means of classified and comparative research, this paper reveals the shift in the development pattern of China’s natural gas utilization structure from the domination of industrial gas and chemical gas to a relative balance among industrial fuel, urban gas, and power generation gas. Sustained and steadily growing natural gas is an important basis for the rapid growth of natural gas utilization. Maintaining moderate growth in the natural gas transmission and distribution network is an important guarantee for the rapid growth of natural gas utilization. The slowdown in the world’s economic growth has not caused any significant adverse impact on the rapid growth in China’s electricity generation gas. The natural gas utilization policy has no effect on the adjustment of industrial fuel gas any longer. Industrial fuels and chemical gas are greatly subject to natural gas prices. Based on China’s energy consumption targets for 2020 and 2030, this paper estimates the gas consumption of major gas areas and their development position, namely, more development space for industrial fuels, still rapid growth in urban gas but hardly major breakthroughs in gas proportion, rapid growth in power generation gas and a more prominent role in peak shaving, sustained high growth in transportation gas and challenges from new energy and low oil prices, and a significant decline but still a large scale in chemicalgas. Keywords: Natural gas utilization, Structure distribution, Influencing factors, Development trends, Development positionin

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model

The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO) algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1)) and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China’s natural gas consumption will grow rapidly over the next five years and reach 354.1 billion cubic meters (bcm) by 2020. Moreover, the spatial distribution of natural gas consumption will shift from being supply oriented towards being demand driven and will be mainly concentrated in coastal and developed provinces

Integrated Evaluation Method-Based Technical and Economic Factors for International Oil Exploration Projects

Optimizing international oil exploration projects is one of the main challenges for oil companies in obtaining investment benefits. This paper establishes an integrated evaluation model to maximize investment benefits within the constraints of technical and economic factors, including geological factors, resource quality, geographic conditions, the investment environment, and oil contracts. The paper also proposes a dynamic calculation method of indicators’ weight associated with oil prices. The analysis describes the effects of contract terms and the investment environment on project value and quantifies the contractor income ratio for different types of contracts and the investment environment of the host country. Oil exploration projects in Africa are illustrated as examples in which the evaluation indicator Adjusted Concept Reserves (ACR) is calculated for each project. The results show that remaining recoverable reserves and contract terms exert tremendous influences on ACR, and remaining recoverable reserves is the essential factor. Simultaneously, changes in oil prices lead to various rates of change in the contractor income ratio, which is determined by different fiscal terms. This study is important in helping oil companies optimize international oil projects and design reasonable investment strategies