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Subseasonal prediction of weekly precipitation anomalies over Southern China during early summer based on predictability analysis and machine learning

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ATMOSPHERIC RESEARCH 2025年 315卷

作者： Lyu, Yang Zhi, Xiefei Zhu, Shoupeng Fan, Yi Ji, Yan Nanjing Univ Informat Sci & Technol Key Lab Meteorol Disaster Collaborat Innovat Ctr Forecast & Evaluat Meteorol Minist Educ KLMEJoint Int Res Lab Climate Environ Nanjing Peoples R China Nanjing Joint Inst Atmospher Sci Key Lab Transportat Meteorol China Meteorol Adm Nanjing Peoples R China Wuxi Univ Sch Atmosphere & Remote Sensing Wuxi Peoples R China

Rainfall in southern China (SC) reaches its annual peak in early summer (May-June), which is often accompanied by the occurrence of rainstorm and flooding disasters. Reliable subseasonal precipitation forecasts play important roles in disaster prevention and mitigation. In this study, the four leading predictable patterns (accounts for 63.24 % of the total variance) of weekly precipitation anomalies (WPA) over Southern China (SC) during early summer and the corresponding signal sources in European Centre for Medium-Range Weather Forecasts (ECMWF) are analyzed. The first and second predictable patterns present uniform and meridional dipole pattern over SC, respectively, while the third and fourth predictable patterns feature with the tripole patterns. Further analysis indicates that the low-level anomalous anticyclone centered over the tropical western Pacific and the lower-level subtropical western North Pacific anomalous anticyclone are the key predictability sources for the first and second predictable patterns, respectively. Although ECMWF shows limited forecast skills in predicting the four leading predictable patterns after 2-week lead time, it can capture the associated large-scale circulation features to a large extent even up to 4-week lead time. Against this backdrop, we develop a dynamical-statistical model (DSM) using random forest to reconstruct these predictable patterns of WPA based on the associated largescale features in ECMWF. The DSM can extend the forecast skills of WPA up to 4-week lead time over the majority area of SC and increase regional average TCC by similar to 0.1 compared to the ensemble mean forecast from ECMWF for 3-4-week lead times. Further analysis indicates that the enhanced TCCs of DSM compared with ECMWF can be largely attributed to the enhancement in predicting the second predictable pattern. The proposed DSM is very promising in subseasonal precipitation forecasts and can also be applied to the routine forecast of other atmosphe

关键词： Subseasonal precipitation forecasts predictability analysis Machine learning Dynamical-statistical model

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predictability analysis of the Pound's Brexit exchange rates based on Google Trends data

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JOURNAL OF BIG DATA 2020年第1期7卷 79页

作者： Mavragani, Amaryllis Gkillas, Konstantinos Tsagarakis, Konstantinos P. Univ Stirling Fac Nat Sci Dept Comp Sci & Math Stirling FK9 4LA Scotland Univ Patras Dept Business Adm Univ Campus RioPOB 1391 Patras 26500 Greece Democritus Univ Thrace Dept Environm Engn Business & Environm Technol Econ Lab Vas Sofias 12 Xanthi 67100 Greece

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound's predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound's exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound's relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound's exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound's exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.

关键词： Big data Dollar Euro Exchange rates Google Trends Internet behavior Pound sterling predictability analysis

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Ultra-short-term wind speed prediction based on multi-scale predictability analysis

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第2期19卷 741-755页

作者： Wan, Jie Ren, Guorui Liu, Jinfu Hu, Qinghua Yu, Daren Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Heilongjiang Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China

Accurate prediction of ultra-short-term wind speed is important in many applications. Because of the different patterns of wind speed, researchers have indicated that a multi-scale prediction method based on wavelet algorithms could improve forecasting. However, traditional multi-scale methods that directly synthetize results of different-scale components may reduce forecasting accuracy because of error accumulation. In this paper, a multi-scale synthesis strategy that takes into account the predictability of different scales is proposed. As the correlation length of each frequency sub-series is different, prediction models are constructed for each frequency sub-series using different numbers of prediction steps. Finally, based on two wind farms in different regions of China and different basic forecast models, eight experiments are performed using real-world wind speed data. Experimental results show that the proposed multi-scale synthesis method has better performance than traditional multi-scale forecasting methods that use direct synthetizing strategies, indicating its utility in wind engineering applications.

关键词： Ultra-short-term prediction Wind speed prediction Wavelet Frequency multi-scale analysis predictability analysis

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Delay-incorporating observability and predictability analysis of safety-critical continuous-time systems

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IET CONTROL THEORY AND APPLICATIONS 2015年第11期9卷 1692-1699页

作者： Eskandari, Neda Dumont, Guy A. Wang, Z. Jane Univ British Columbia Dept Elect & Comp Engn Vancouver BC V5Z 1M9 Canada

The authors suggest a framework for human-automation interaction in safety-critical continuous systems under shared control and consider continuous-time linear time-invariant (LTI) dynamics to formalise our physical models mathematically. Their goal is to determine whether or not a given user-interface provides the information required for a certain task, under the assumption that the user does not have access to any information beyond what is provided in the display. They identify observability-based conditions under which a user-interface provides the user with necessary information to accomplish a given task, formulated as a subset of the state space. They, therefore formulate the novel delay-incorporating user-observable subspace, and the delay-incorporating user-predictable subspace and compare them with the space spanned by the combination of the states which create the task. They assume the user is a special type of observer, with capabilities corresponding to different levels of knowledge regarding the current user's input and its derivatives. In addition, they consider that state reconstruction and prediction incorporate a processing delay.

关键词： observability continuous time systems safety-critical software delays observers state-space methods human computer interaction prediction theory control engineering computing human automation interaction continuous time LTI dynamics observability-based condition identification user interface state space method observer state reconstruction state prediction processing delay observability analysis predictability analysis safety-critical continuous time systems delay incorporating user observable subspace delay incorporating user predictable subspace

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Multiple Temporal Pattern Detection And predictability analysis of Complex Time-Evolving Systems

Multiple Temporal Pattern Detection And Predictability Analy...

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7th World Congress on Intelligent Control and Automation

作者： Feng, Xin Senyana, Odilon K. Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA

ISBN: (纸本)9781424421138

This paper presents a new method, called Multiple Temporal Pattern Recognition (MTPR), that is capable of detecting multiple temporal patterns for characterizing and predicting events of interest in the time-evolving system data. The MTPR method embeds time series data into multiple phase spaces with various dimensions and time delays. Then it clusters the embedded data to detect the preliminary temporal patterns. We further performed a three-stage statistical predictability analysis to evaluate the confidence of the detected temporal patterns. At the first stage, we introduced a new predictability Measure, p., to evaluate the temporal patterns and then apply the statistical logistical regression to further validate these patterns. Experimental results are also included to illustrate the MTPR method.

关键词： Temporal Pattern Time Series Data Mining Phase Space Embedding Statistical Logistic Regression predictability analysis

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Link predictability analysis of US Political Blog Network with Structural Perturbation Method 6

Link Predictability Analysis of US Political Blog Network wi...

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6th International Symposium on Computational and Business Intelligence (ISCBI)

作者： Yang, Yuling Zhou, Yun Chen, Guangquan Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781538694503

Many link prediction methods have been developed in past few decades in network science, and most of them are tailored for specific fields that lose generalities. Luckily, a recent work named Structural Perturbation Method (SPM) proposed a consistency index of network organization without priori knowledge, and it did not need to test those predicting methods first. Since demonstrating whether there is a link between two nodes is usually costly, we want to replace link confirmation with link prediction. In this paper, we use the SPM method to study the structure of US political blog network and analyze the link predictability at different network scales. The experimental results show that we can obtain basic intrinsic features of the network structure and can get ideal prediction results as long as 20% of the network structures.

关键词： complex network link prediction predictability analysis

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Multiple Temporal Pattern Detection And predictability analysis of Complex Time-Evolving Systems

Multiple Temporal Pattern Detection And Predictability Analy...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.4

作者： Xin Feng Odilon K. Senyana Department of Electrical and Computer Engineering Marquette University Milwaukee WI USA

This paper presents a new method, called Multiple Temporal Pattern Recognition (MTPR), that is capable of detecting multiple temporal patterns for characterizing and predicting events of interest in the time-evolving system data. The MTPR method embeds time series data into multiple phase spaces with various dimensions and time delays. Then it clusters the embedded data to detect the preliminary temporal patterns. We further performed a three-stage statistical predictability analysis to evaluate the confidence of the detected temporal patterns. At the first stage, we introduced a new predictability Measure, p{sub}m, to evaluate the temporal patterns and then apply the statistical logistical regression to further validate these patterns. Experimental results are also included to illustrate the MTPR method.

关键词： Temporal Pattern Time Series Data Mining Phase Space Embedding Statistical Logistic Regression predictability analysis

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Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

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JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS 2014年第6期41卷 553-569页

作者： Marostica, Eleonora Russu, Alberto Yang, Shuying De Nicolao, Giuseppe Zamuner, Stefano Beerahee, Misba Univ Pavia Dept Ind & Informat Engn I-27100 Pavia Italy GlaxoSmithKline Clin Pharmacol Modelling & Simulat Stockley Pk England

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient's health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response;(ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties;(iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.

关键词： Asthma Population approach Meta-analysis predictability analysis Covariate analysis

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Climate risks and the REITs market

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INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS 2025年第2期30卷 1632-1648页

作者： Salisu, Afees A. Ogbonna, Ahamuefula E. Vo, Xuan Vinh Univ Econ Ho Chi Minh City Inst Business Res Ho Chi Minh City Vietnam Ctr Econometr & Appl Res 5 Oba AkinyeleDPC RdOjetunji Aboyades House Ibadan Oyo Nigeria Univ Ibadan Ctr Econometr & Appl Res Ibadan Oyo Nigeria Univ Ibadan Dept Stat Ibadan Oyo Nigeria Univ Econ Ho Chi Minh City CFVG Ho Chi Minh City Ho Chi Minh City Vietnam

This study presents results supporting the need to price climate risks in real estate investment trusts. We approach this objective by conducting some empirical analyses for global, regional, and [US] sectoral REITs for want of wider coverage while we also consider variants of climate risks involving physical and transition risks. We first establish that climate concerns amplify the volatility of REIT returns. While our results are split for sectoral REITs, we find that both physical and transition risks magnify the volatility in the regional and global REITs market. However, when we consider the US sectoral REITs, we find contrasting evidence between the two variants of climate risks. While the transition risks seem to raise the REITs market volatility, perhaps owing to improved trading in the market as signalled by some level commitments towards addressing climate change, the physical risks associated with damages due to climate change tend to lower the REITs market volatility due to lower trading. Consequently, we formulate a framework that enables a profit-maximizing investor to observe climate risks when making investment decisions in the REITs market, and we further show that doing so provides higher economic gains than ignoring it. This outcome has implications for investors looking for the best hedging strategy against climate-related risks, especially as the world intensifies efforts towards de-carbonization.

关键词： climate risks forecast evaluation hedging predictability analysis REITs volatility

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Intra and inter-series pattern representations fusion network for multiple time series forecasting

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Jin, Canghong Chen, Tianyi Ni, Hao Shi, Qihao Hangzhou City Univ Hangzhou 310015 Peoples R China Fudan Univ Shanghai 200433 Peoples R China Zhejiang Univ Technol Hangzhou 310014 Peoples R China

Multiple time series (MTS) can comprise data collected from various wireless sensor networks in the actual application, and each source provides a distinctive pattern. Most existing neural network methods attempt to model the patterns of individual time series by training a global model using the entire dataset, suffering from insufficient ability to consider the differences among source patterns and lowering the predictability. To address this limitation, we propose the Multiple Time Series Pattern Representation Network(MTS-PRNet), a unified framework consisting of two modules to forecast multiple time series from diverse sources. The first is the intra-series correlation learning module, which explicitly learns the temporal dependencies of time series. The second is the inter-series discriminative representation learning module that learns shapelets as discriminative representations to capture shared features among series. By integrating the covariates map generated by the second module, both intra and inter-series characteristics are captured to provide transferable guidance for increasing predictability. Experiments conducted on 9 datasets verify that our model achieves state-of-theart performance. In particular, we carry out an ablation study to validate the effectiveness of discriminative representations.

关键词： Multiple time series forecasting Pattern representation predictability analysis Context encoding

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