Intertemporal Dependence Research Articles

On the estimation of bivariate conditional transition rates

Recent literature has found conditional transition rates to be a useful tool for avoiding Markov assumptions in multi-state models. While the estimation of univariate conditional transition rates has been extensively studied, the intertemporal dependencies captured in the bivariate conditional transition rates still require a consistent estimator. We provide an estimator that is suitable for censored data and emphasize the connection to the rich theory of the estimation of bivariate survival functions. Bivariate conditional transition rates are necessary for various applications in the survival context but especially in the calculation of moments in life insurance mathematics.

Enhancing P-wave localization for accurate detection of second-degree and third-degree atrioventricular conduction blocks

Objective.This paper tackles the challenge of accurately detecting second-degree and third-degree atrioventricular block (AVB) in electrocardiogram (ECG) signals through automated algorithms. The inaccurate detection of P-waves poses a difficulty in this process. To address this limitation, we propose a reliable method that significantly improves the performances of AVB detection by precisely localizing P-waves.Approach.Our proposed P-WaveNet utilized an attention mechanism to extract spatial and temporal features, and employs a bidirectional long short-term memory module to capture inter-temporal dependencies within the ECG signal. To overcome the scarcity of data for second-degree and third-degree AVB (2AVB,3AVB), a mathematical approach was employed to synthesize pseudo-data. By combining P-wave positions identified by the P-WaveNet with key medical features such as RR interval rhythm and PR intervals, we established a classification rule enabling automatic AVB detection.Main results. The P-WaveNet achieved an F1 score of 93.62% and 91.42% for P-wave localization on the QT Dataset and Lobachevsky University dataset datasets, respectively. In the BUTPDB dataset, the F1 scores for P-wave localization in ECG signals with 2AVB and 3AVB were 98.29% and 62.65%, respectively. Across two independent datasets, the AVB detection algorithm achieved F1 scores of 83.33% and 84.15% for 2AVB and 3AVB, respectively.Significance.Our proposed P-WaveNet demonstrates accurate identification of P-waves in complex ECGs, significantly enhancing AVB detection efficacy. This paper's contributions stem from the fusion of medical expertise with data augmentation techniques and ECG classification. The proposed P-WaveNet demonstrates potential clinical applicability.

Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities

This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.

Investment centre manager's multiperiod fairness perceptions and intertemporal dependency

AbstractThis paper explores the motivation of investment centre managers when their investment centre's performance is affected by decisions made by their predecessor. Through a qualitative case study of a Japanese manufacturer, the effectiveness of conventional remedies for motivational issues and further motivational issues caused by the same remedies, as identified in the extant literature, are examined. The field data underscore managers' multiperiod as opposed to period‐by‐period fairness perceptions as key to preventing the further motivational issues. This paper also demonstrates the potential usefulness of the vignette technique as a data collection method in qualitative accounting research.

Restructuring of units under inter-temporal dependence: method and application

Restructuring of units under inter-temporal dependence: method and application

Renewable energy forecasting: A self-supervised learning-based transformer variant

Reliable and accurate renewable energy forecasting (REF) has substantial impact on society by helping with daily planning and mitigating the instability of power system. Providing a state-of-the-art (SOTA) solution for REF can power it to take on more sophisticated tasks and solve frontier problems to play a greater role in modern society. Aiming this goal, this work proposes a novel Transformer-based model, named Graph Patch Informer (GPI), for REF. Compared with existing REF models and mainstream Transformer, our model has three main characteristics: (1) Segment-wise self-attention is designed, which benefits Transformer by preserving the temporal information hidden in the continuous signals, (2) Graph Attention Networks with adaptive adjacent matrix is proposed to capture the inter-temporal dependencies automatically, (3) A new training strategy, that is, self-supervised pre-training followed by fine-tuning, is introduced to enhance the representation learning. To validate the performance of GPI, five experiments are conducted on four datasets covering solar radiation (SR), photovoltaic power (PVP), wind speed (WS) and wind power (WP). Experiments show that GPI goes beyond SOTA Autoformer by 23.67%–40.75% on MSE. It demonstrates that GPI can provide an effective solution for REF and have SOTA performance on SR, PVP, WS, WP forecasting tasks. Experiments also show that GPI can mitigate the adverse influences caused by missing values to a certain degree.

Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method

Advanced stochastic programming-based power system operations planning requires wind power forecast in the form of scenarios. Generating wind power scenarios reflecting the intertemporal dependence over the forecast horizon is paramount for multi-period operations planning routines. Yet, less attention has been given to such time-coupled (temporal) wind power scenario generation (SG). Recent literature shows that copula-based SG methods are suitable for typical operations planning routines like economic dispatch and unit commitment. This work proposes a new and efficient data-driven temporal wind power SG framework using regular vine copula with variance reduction. The proposed SG puts forth two contributions to improve the quality of the temporal scenarios. The first contribution is to introduce the regular vine copula to model the temporal dependence structure of the wind power forecast error, which is shown to fit the real-world data better than the existing copula models. The second contribution is to propose a uniform design-based vine copula sampling algorithm, which benefits the downstream operations planning applications with improved convergence and accuracy of the solutions. A detailed multivariate scenario evaluation using multiple metrics shows that the proposed SG improves the quality of the temporal scenarios compared to the existing benchmarks. The Diebold-Mariano statistical test also verifies the significant improvement in the quality of the wind power scenarios.

Flexibility Aggregation of Temporally Coupled Resources in Real-Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio’s actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator’s offer. Once trained, the ML algorithms can make fast decisions about the portfolio’s balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator’s portfolio and minimizing the aggregator’s imbalances.

Disentangling the impact of mean reversion in estimating policy response with dynamic panels

Abstract This article accounts for multivariate dependence of the variable of policy interest in dynamic panel data models by disentangling the two sources of intertemporal dependence: one from the effect of the policy variable and the other from mean reversion. In a situation where intensity of the policy varies over time, we estimate the unconditional mean in the autoregressive process as a function of the agent’s characteristics and the policy intensity. Comparison of the fitted values of the unconditional mean under different values of the policy intensity enables identification of the policy effect cleared of mean reversion. The approach is relevant for measuring the effect of reforms, which use an intertemporal incentive where intensity of the reform varies over time. The empirical part of the article assesses the effect of hospital financing reform based on incentive contracts, related to the observed quality of services at Medicare hospitals in 2013–2019. We find a direct association between prior quality and quality improvement owing to the reform. Our result reassesses a stylized fact in the literature, which asserts that a pay-for-performance incentive leads to greater improvements at hospitals with lower baseline quality.

A new method of determining decision-making unit congestion under inter-temporal dependence

Congestion is an essential concept in data envelopment analysis (DEA). It occurs when the decrease in at least one decision-making unit (DMU) input increases at least one output without affecting the other inputs or outputs. Although various methods have discussed congestion determination in static DEA, congestion calculation in dynamic DEA under inter-temporal dependence has not been considered in the literature. This paper deals with the congested input levels in multi-period production systems under inter-temporal dependence and the inter-temporal dependence of production periods due to the stock capital. Due to the importance of evaluating DMUs in the presence of time factors, this paper expands the determination of congestion of DMUs under the time factor and dependence of input and output levels in periods. The proposed models determine the congested unit in the assessment window and characterize the extra-input and lack-output amounts in all of the congested unit components in each period of the assessment window. This paper aims to specify production system performance in different evaluation periods by determining the dynamic congestion of DMUs. This paper proposes a method to identify the congested paths. The main advantage of this method is to prevent inappropriate allocation of the resources during evaluation periods of the assessment window. The validity of the proposed method is investigated through an example in the banking sector. Moreover, this method can be utilized in other sectors to homogeneous units with the same structure.

How to Prevent Vaccines Falling Victim to Their Own Success: Intertemporal Dependency of Incidence Levels on Indirect Effects in Economic Reevaluations

ObjectivesIncremental cost-effectiveness analyses may inform the optimal choice of healthcare interventions. Nevertheless, for many vaccines, benefits fluctuate with incidence levels over time. Reevaluating a vaccine after it has successfully decreased incidences may eventually cause a disease resurgence if switching to a vaccine with lower indirect benefits. Decisions may successively alternate between vaccines alongside repeated rises and falls in incidence and when indirect effects from historic use are ignored. Our suggested proposal aims to prevent suboptimal decision making. MethodsWe used a conceptual model of demand to illustrate alternating decisions between vaccines because of time-varying levels of indirect effects. Similar to the concept of subsidies, we propose internalizing the indirect effects achievable with vaccines. In a case study over 60 years, we simulated a hypothetical 10-year reevaluation of 2 oncogenic human papillomavirus vaccines, of which only 1 protects additionally against anogenital warts. ResultsOur case study showed that the vaccine with additional warts protection is initially valued higher than the vaccine without additional warts protection. After 10 years, this differential decreases because of declines in warts incidence, which supports switching to the nonwarts vaccine that causes a warts resurgence eventually. Instead, pricing the indirect effects separately supports continuing with the warts vaccine. ConclusionsIgnoring how the observed incidences depend on the indirect effects achieved with a particular vaccine may lead to repeated changes in vaccines at successive reevaluations, with unintended resurgences, economic inefficiencies, and eroding vaccine confidence. We propose internalizing indirect effects to prevent vaccines falling victim to their own success.

Cost-efficiency under inter-temporal dependence

One method for evaluating cost-efficiency of commercial firms is data envelopment analysis. In the real world, data have a time dependency that also affects the cost-efficiency calculation. This article introduces a new model for measuring cost-efficiency under inter-temporal dependency in an assessment window. In the proposed approach, there is a reserve capital for the assessment window that is considered as the input of this period, and at any time of the window, a certain amount of capital is used. For practical application, the proposed model is applied to a real dataset of branches of an Iranian commercial bank to evaluate cost-efficiency under inter-temporal dependency.

Restructuring of units under inter-temporal dependence: Method and application

Restructuring of units under inter-temporal dependence: Method and application

A novel decentralized platform for peer-to-peer energy trading market with blockchain technology

Peer-to-Peer (P2P) energy trading, which allows energy consumers/producers to directly trade with each other, is one of the new paradigms driven by the decarbonization, decentralization, and digitalization of the energy supply chain. Additionally, the rise of blockchain technology suggests unprecedented socio-economic benefits for energy systems, especially when coupled with P2P energy trading. Despite such future prospects in energy systems, three key challenges might hinder the full integration of P2P energy trading and blockchain. First, it is quite complicated to design a decentralized P2P market that keeps a fair balance between economic efficiency and information privacy. Secondly, with the proliferation of storage devices, new P2P market designs are needed to account for their inter-temporal dependencies. Thirdly, a practical implementation of blockchain technology for P2P trading is required, which can facilitate efficient trading in a secured and fraud-resilient way, while eliminating any intermediaries’ costs. In this paper, we develop a new decentralized P2P energy trading platform to address all the aforementioned challenges. Our platform consists of two key layers: market and blockchain. The market layer features a parallel and short-term pool-structured auction and is cleared using a novel decentralized Ant-Colony Optimization method. This market arrangement guarantees a near-optimally efficient market solution, preserves players’ privacy, and allows inter-temporal market products trading. The blockchain layer offers a high level of automation, security, and fast real-time settlements through smart contract implementation. Finally, using real-world data, we simulate the functionality of the platform regarding energy trading, market clearing, smart contract operations, and blockchain-based settlements.

Efficiency and super-efficiency under inter-temporal dependence

In this paper, a linear programming (LP) model for measuring the relative efficiency of a decision-making unit (DMU) under inter-temporal dependence of data is proposed. Necessary and sufficient conditions are derived for identification of dynamically efficient paths. Furthermore, an LP model is proposed to estimate the super-efficiency of the dynamically efficient paths using an extended version of the modified MAJ model (Saati et al., Ric. Oper. 31 (2001) 47–59). To evaluate the applicability of the proposed method in a banking sector example, this method is employed for ranking some branches of the Iranian commercial bank.

Learning Operational Decisions with Intertemporal Dependence and Moderate Non-stationarities

This work focuses on solving the data-driven contextual newsvendor problem with intertemporal dependence and non-stationarities. More specifically, we investigate learn the data-to-decision mapping for the newsvendor problem when observations of contexts and demands are available. The observations of both contexts and demands are generated sequentially in a fluctuate nature, thus exhibit an intertemporal dependence and even non-stationarities. However, most existing works that investigate the data-driven conditional Newsvendor problem adopt a common assumption that the data are independent and identically distributed (i.i.d.) to obtain performance guarantees such as generalization bounds. In this work, we develop performance guarantees in the form of out-of-sample generalization bounds for learning contextual newsvendor problem under comparatively more realistic assumptions including intertemporal dependence and moderate non-stationarities.

A generalized DEA model for inputs (outputs) estimation under inter-temporal dependence

This paper extended the inverse Data Envelopment Analysis (DEA) to the framework of dynamic DEA. The following question is studied under inter-temporal dependence assumption: among a set of decision making units (DMUs), to what extent should the input (output) levels of the DMU change if the efficiency index of a DMU remains unchanged, yet the output (input) levels change? This question is answered using (periodic weak) Pareto solutions of multiple-objective linear programming (MOLP) problems in the framework of dynamic DEA. In this study, unlike other proposed methods, the simultaneous increase and decrease of the various input (output) levels are considered under inter-temporal dependence. In addition, a numerical example with real data is provided to illustrate the objective of this research.

Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit intertemporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.

Dynamic fuzzy data envelopment analysis models: case of bus transport performance assessment

In the transport field, two characteristics–inter-temporal dependency and fuzziness–need to be considered when assessing transport performance. First, input and output levels are inter-temporal dependent due to heavy capital investment and because quasi-fixed input can influence output levels over multiple periods. Second, conventional Data Envelopment Analysis (DEA) models are, in nature, formulated with quantitative variables. However, qualitative measurements that are characterized with “vagueness” or “fuzziness” are as important as quantitative variables for multi-period transport performance assessment. To rectify these problems, the present study extends previous research by proposing a Dynamic Fuzzy Data Envelopment Analysis (DFDEA) method for assessing the comparative efficiency where inter-temporal dependence exists in operating production processes with some “fuzzy” variables. An case study was conducted to evaluate the performance of city bus transport companies in Taipei, Taiwan. Results showed the superiority of the proposed DFDEA model by comparing the results with static models.

Characterizing the Tail-Risk of Factor Mimicking Portfolios

By assuming that short-run returns are independent and identically distributed, it is straightforward to extrapolate short-run risks to longer horizons. However, by generalizing the variance-ratio test to include higher co-moments, we establish a significant and sizable intertemporal dependency in all higher moments of equity returns. The intertemporal dependency is strong enough to prevent the convergence to normally distributed returns, at least up to a five-year holding period. We also demonstrate that the intertemporal dependency is both horizon \emph{and} portfolio-specific. Consequently, the common practice of extrapolating the short-run risk by assuming independent and identically distributed returns will severely bias the expected long-run risk.