scholarly journals Mortality Forecasting with an Age-Coherent Sparse VAR Model

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 35
Author(s):  
Hong Li ◽  
Yanlin Shi
Keyword(s):  
Coefficient Matrix ◽  
Var Model ◽  
Mortality Data ◽  
Invariant Mean ◽  
Short Term ◽  
Efficient Manner ◽  
Mortality Models ◽  
Proposed Model ◽  
Mortality Improvement

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs a rotation algorithm in the projection phase to generate age-coherent mortality forecasts. In the estimation phase, the age-specific mortality improvement rates are fitted to a VAR model with dimension reduction algorithms such as the elastic net. In the projection phase, the projected mortality improvement rates are assumed to follow a short-term fluctuation component and a long-term force of decay, and will eventually converge to an age-invariant mean in expectation. The age-invariance of the long-term mean guarantees age-coherent mortality projections. The proposed model is generalized to multi-population context in a computationally efficient manner. Using single-age, uni-sex mortality data of the UK and France, we show that the proposed model is able to generate more reasonable long-term projections, as well as more accurate short-term out-of-sample forecasts than popular existing mortality models under various settings. Therefore, the proposed model is expected to be an appealing alternative to existing mortality models in insurance and demographic analyses.

scholarly journals Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

2020 ◽  
Vol 10 (11) ◽  
pp. 3712
Author(s):  
Dongjing Shan ◽  
Xiongwei Zhang ◽  
Wenhua Shi ◽  
Li Li
Keyword(s):  
State Of The Art ◽  
Cell Structure ◽  
Frequency Discrimination ◽  
Search Space ◽  
Short Term ◽  
Cell Parameters ◽  
Neural Architecture ◽  
Proposed Model ◽  
Initialization Scheme

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

scholarly journals Modeling sensitivity indices of industrial enterprise organizational change

2021 ◽  
Vol 10 (45) ◽  
pp. 230-241
Author(s):  
Victoriia Bilyk ◽  
Olena Kolomytseva ◽  
Olha Myshkovych ◽  
Nataliia Tymoshyk ◽  
Denis Shcherbatykh
Keyword(s):  
Organizational Change ◽  
Theory And Practice ◽  
Organizational Changes ◽  
Short Term ◽  
Industrial Enterprises ◽  
Sensitivity Indices ◽  
Proposed Model ◽  
Development Effectiveness ◽  
Made In

Evaluation of sensitivity of commercial enterprises to organizational changes should be made in terms of short-term planning for which it is important to ensure the financial results, as well as in terms of long-term planning, which is important for non-monetary indicators of development effectiveness. To solve this problem, the paper is designed model sensitivity Descriptive indicators of industrial enterprises to organizational changes, reflecting monetary and non-monetary effects of organizational change. The authors determined that the proposed model allows for the analysis of organizational change with regard to their impact on monetary and non-monetary efficiency. This paper contributes to the theory and practice at the border to ensure a balance between short-term and long-term development of industrial enterprises. Convincingly demonstrated the possibility of using research results in practice.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

scholarly journals Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4804
Author(s):  
Rui Cao ◽  
Jianjian Shen ◽  
Chuntian Cheng ◽  
Jian Wang
Keyword(s):  
Power Generation ◽  
Optimization Model ◽  
Hydropower Plant ◽  
Mixed Integer ◽  
Short Term ◽  
Long Distance ◽  
Peak Shaving ◽  
Term Operation ◽  
Proposed Model

The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.

An analytical approach for behavioral portfolio model with time discounting preference

2018 ◽  
Vol 52 (3) ◽  
pp. 691-712
Author(s):  
Guang Yang ◽  
Xinwang Liu ◽  
Jindong Qin ◽  
Ahmed Khan
Keyword(s):  
Portfolio Selection ◽  
Investment Strategy ◽  
Cumulative Prospect Theory ◽  
Investment Strategies ◽  
Short Term ◽  
Time Discounting ◽  
Present Bias ◽  
Proposed Model ◽  
Portfolio Selection Model

This paper presents a behavioral portfolio selection model with time discounting preference. Firstly, we discuss the portfolio selection problem and then modify this model based on cumulative prospect theory (CPT) as well as considering investors’ time discounting preference in psychology. Furthermore, an analytical solution with satisfying behavior is given for our proposed model, the results show that when investors’ goals are very ambitious, they put a high proportion of their wealth in long-term goals and adopt aggressive investment strategies with high leverage to reach short-term goals and the overall investment strategy also displays high leverage. Finally, numerical analysis is given and it is shown that investor who tends to future bias performs adequate confidence and patience whereas investor with present bias is apt to the immediate interests.

R&D PERFORMANCE MONITORING, EVALUATION, AND MANAGEMENT SYSTEM: A MODEL AND METHODS

2011 ◽  
Vol 08 (02) ◽  
pp. 295-313 ◽  
Author(s):  
HAKYEON LEE ◽  
MOON-SOO KIM ◽  
SOUNG RYONG YEE ◽  
KYUNGIL CHOE
Keyword(s):  
Management System ◽  
Performance Monitoring ◽  
Data Envelopment ◽  
Short Term ◽  
Analytic Hierarchy ◽  
Public Programs ◽  
System A ◽  
Proposed Model ◽  
Hierarchy Process

Since previous frameworks and methods for evaluation of national R&D have mainly focused on short-term R&D outputs, long-term R&D outcomes have been neglected so that they have failed to be fully utilized and spread. Thus, it is imperative to mirror long-term R&D outcomes in performance evaluation and utilize them through continuous management and monitoring. In response, this paper presents a model of R&D performance monitoring, evaluation, and management system (RPMEMS) for national R&D. The conceptual model of RPMES is presented with its scope, structure, role, and process. It is based on the logic model used in the planning and evaluation of public programs. This study also proposes two methods for relative comparison in the context of RPMEMS: analytic hierarchy process (AHP) and data envelopment analysis (DEA). The proposed model of RPMEMS and methods for relative comparison are expected to be effectively implemented and operated in the practice of management of national R&D.

scholarly journals A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting

2017 ◽  
Vol 11 (2) ◽  
pp. 343-389 ◽  
Author(s):  
Man Chung Fung ◽  
Gareth W. Peters ◽  
Pavel V. Shevchenko
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Stochastic Volatility ◽  
Model Complexity ◽  
Mortality Data ◽  
Period Effect ◽  
Male Mortality ◽  
Mortality Models ◽  
Volatility Model

AbstractThis paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee–Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and forecasting of mortality under a unified framework. We propose alternative model identification constraints which are more suited to statistical inference in filtering and parameter estimation. We then develop a class of Bayesian state-space models which incorporate a priori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data. To study long-term mortality dynamics, we introduce stochastic volatility to the period effect. The estimation of the resulting stochastic volatility model of mortality is performed using a recent class of Monte Carlo procedure known as the class of particle Markov chain Monte Carlo methods. We illustrate the framework using Danish male mortality data, and show that incorporating heteroscedasticity and stochastic volatility markedly improves model fit despite an increase of model complexity. Forecasting properties of the enhanced models are examined with long-term and short-term calibration periods on the reconstruction of life tables.

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