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.

scholarly journals The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 69
Author(s):  
Guoliang Feng ◽  
Wei Lu ◽  
Jianhua Yang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Least Square ◽  
Fuzzy Cognitive Map ◽  
Statistical Characteristics ◽  
Series Data ◽  
The Impact

A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time series. A fuzzy c-means clustering algorithm is used to construct the concepts of the FCM. Compared with the traditional FCM, the least square fuzzy cognitive map (LSFCM) is a direct solution procedure without iterative calculations. LSFCM model is a straightforward, robust and rapid learning method, owing to its reliable and efficient. In addition, the structure of the LSFCM can be further optimized with refinements the position of the concepts for the higher prediction precision, in which the evolutionary optimization algorithm is used to find the optimal concepts. Withal, we discussed in detail the number of concepts and the parameters of activation function on the impact of FCM models. The publicly available time series data sets with different statistical characteristics coming from different areas are applied to evaluate the proposed modeling approach. The obtained results clearly show the effectiveness of the approach.

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.

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

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.

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

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.

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