Decision Making Strategy Based on Time Series Data of Voting Behavior

2015 ◽  
pp. 229-241
Author(s):  
Shogo Higuchi ◽  
Ryohei Orihara ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Voting Behavior ◽  
Time Series Data ◽  
Series Data

scholarly journals Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

scholarly journals Analysis of a long-term Forest Accountancy Network to Support Management and Controlling

2018 ◽  
Vol 3 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Markus Dög ◽  
Johannes Wildberg ◽  
Bernhard Möhring
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Management Strategies ◽  
Series Data ◽  
Forest Economics ◽  
Private Forest ◽  
Forest Landowners ◽  
Forest Enterprises

Abstract Multifunctional forestry in Germany is characterized by long production periods and complex biological-technical processes. Private forest enterprises are complex systems which are closely interwoven with the economic environment. To ensure their economic success, forest landowners need to take the economic development into consideration and adapt their management strategies. Management accounting is an important source for information needed to fulfil main tasks of accounting that help to manage forest enterprises: ‘description’, ‘explanation’ and ‘decision making’. To get general data, long time series data, taken from Forest Accountancy Networks (FAN), can be analysed. For more than 45 years, data from the FAN Westfalen-Lippe in Germany has been collected and analysed by the department of Forest Economics and Forest Management at the University of Göttingen. The long-term development and adaptation strategies of defined groups of private forest enterprises can be illustrated using this data. These valuable time series can support decision-making processes for private forest landowners and provide tools for forest policy. The data shows that private forest enterprises, with spruce as the dominating tree species, have performed above average in terms of operating revenues and profit margins, but are also more susceptible to calamities resulting in higher involuntary timber harvests.

scholarly journals Prediction of Interbank Offered Rate Based on Time Series Model

2021 ◽  
Vol 251 ◽  
pp. 01014
Author(s):  
Ding Huang ◽  
Ming Zhong ◽  
Xupeng Shi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Model ◽  
Grey Model ◽  
Actual Data ◽  
Series Data ◽  
Bank Management ◽  
Working Day ◽  
The Impact

This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

scholarly journals A hidden Markov model analysis of subject-specific seizure time-series data as a potential aid to clinical decision making

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2592
Author(s):  
Martin D. King ◽  
Suresh Pujar ◽  
Rod C. Scott
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Sojourn Time ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Seizure Activity ◽  
Hidden Markov ◽  
Clinical Decision ◽  
Series Data ◽  
Subject Specific

Background The seizure-count time series data acquired from three children with refractory epilepsy were used in a statistical modelling analysis designed to provide an explanation for the marked variation in seizure frequency that often occurs over time (over-dispersed Poisson behaviour). This was motivated by an expectation that a better understanding of the spontaneous shifts in seizure-activity that are observed in some cases should reduce the risk of over-treatment caused by inappropriate changes in medication. Methods The analyses were performed using Poisson hidden Markov models (HMMs), both Bayesian and non-Bayesian, implemented using Markov chain Monte Carlo and the expectation-maximisation algorithm, respectively. A defining feature of the models, as applied to epilepsy, is the assumed existence of two or more pathological states, with state-specific Poisson rates, and random transitions between the states. Posterior predictive simulation was used to assess the validity of the Bayesian HMMs. Results The results are presented in the form of state transition probability and Poisson rate estimates (i.e., the primary HMM parameters), together with information derived from these primary parameters. State-specific mean-duration (sojourn time) estimates and sojourn-time complementary cumulative probability distributions are the main focus. HMM analyses are presented for three children that differed markedly in their seizure behaviour. The first is characterised by an extreme seizure count on one occasion; the second underwent a spontaneous decrease in seizure activity during the observation period; the third seizure-count time trajectory is characterised by a gradual change in mean seizure activity. We show that, despite their considerable differences, each of the observed seizure-count trajectories can be treated adequately using an HMM. Conclusions The study demonstrates that clinically relevant information can be obtained using HM modelling in three cases with markedly different seizure behaviour. The resulting subject-specific statistics provide useful clinical insights which should aid those engaged in clinical decision making.

The Entrepreneurial Decision: Economic Theory and Empirical Evidence

1995 ◽  
Vol 19 (4) ◽  
pp. 67-79 ◽  
Author(s):  
Joseph G. Eisenhauer
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Time Series ◽  
Empirical Evidence ◽  
Time Series Data ◽  
Economic Thought ◽  
Series Data ◽  
Entrepreneurial Decision ◽  
The U.S

This paper Integrates three major traditions of economic thought into a model of entrepreneurial decision making. Several testable hypotheses are formulated, and the model Is estimated using a 33-year sample of aggregate time-series data for the U.S.

scholarly journals Automation of Longitudinal Time Series Data Visualization using Plot Specifications obtained from XL file

2019 ◽  
Vol 8 (6) ◽  
pp. 3105-3109
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Complex Data ◽  
Business Decision ◽  
Business Requirement ◽  
Visualization Techniques

Longitudinal Time Series data visualization plays important role in all sector of business decision making [9]. With enormous amount of complex data [11] from cloud and business requirement, number of graphs needed for decision making increased many folds. Generating enormous number of plots manually with more human input is tedious, time consuming and error prone. To avoid these issues, suitable visualization techniques with solid design principles become very important. We conceptualized and designed a novel method for automation of these processes. R-GGPLOT2[7] package and XL specifications file were primarily used to achieve this goal. We here show as how we can create multiple plots from time series data, plots specifications-XL file and R package GGPLOT2[7] in a single run. Since all required information are entered in XL sheet, R function can be run with no modification. Multiple plots can be generated by using enormous data available in production and service sectors such as finance, healthcare, transportation and food industries etc.

scholarly journals Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2059
Author(s):  
Katarzyna Poczeta ◽  
Elpiniki I. Papageorgiou ◽  
Vassilis C. Gerogiannis
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Main Idea ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Series Data ◽  
Nested Structure ◽  
Precise Representation ◽  
Energy Consumption Prediction

Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.

Sign in / Sign up

Export Citation Format

Share Document