Short-Time Prediction Based on Recognition of Fuzzy Time Series Patterns

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

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Short Time Prediction of Chaotic Time Series Using ANFIS

Recent Progress in Computational Sciences and Engineering ◽

10.1201/9780429070655-165 ◽

2019 ◽

pp. 729-733

Author(s):

M. Valyrakis ◽

P. Diplas ◽

C. L. Dancey

Keyword(s):

Time Series ◽

Chaotic Time Series ◽

Time Prediction ◽

Short Time

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About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

Indian Journal Of Applied Research ◽

10.15373/2249555x/sept2013/173 ◽

2011 ◽

Vol 3 (9) ◽

pp. 562-566

Author(s):

Ramin Rzayev ◽

◽

Musa Agamaliyev ◽

Nijat Askerov

Keyword(s):

Time Series ◽

Time Series Model ◽

Fuzzy Time Series

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A strategy for meta-analysis of short time series microarray datasets

Frontiers in Bioscience ◽

10.2741/3512 ◽

2009 ◽

Vol Volume (14) ◽

pp. 4058 ◽

Cited By ~ 2

Author(s):

Ruping Sun

Keyword(s):

Time Series ◽

Meta Analysis ◽

Short Time Series ◽

Microarray Datasets ◽

Short Time ◽

Time Series Microarray

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Peramalan Data IHSG Menggunakan Metode Backpropagation

Jurnal ULTIMATICS ◽

10.31937/ti.v5i1.310 ◽

2013 ◽

Vol 5 (1) ◽

pp. 26-30

Author(s):

Seng Hansun

Keyword(s):

Time Series ◽

Soft Computing ◽

Fuzzy Time Series

Jaringan saraf tiruan merupakan salah satu metode soft computing yang banyak digunakan dan diterapkan di berbagai disiplin ilmu, termasuk analisis data runtun waktu. Tujuan utama dari analisis data runtun waktu adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas dalam berbagai data runtun waktu real, termasuk data harga saham. Banyak peneliti yang telah berkontribusi dalam analisis data runtun waktu dengan menggunakan berbagai pendekatan berbeda. Chen dan Hsu, Jilani dkk., Stevenson dan Porter, dan Hansun telah menggunakan metode fuzzy time series untuk meramalkan data mendatang, sementara beberapa peneliti lainnya menggunakan metode hibrid, seperti yang dilakukan oleh Subanar dan Suhartono, Popoola dkk, Popoola, Hansun dan Subanar. Di dalam penelitian ini, penulis mencoba untuk menerapkan metode jaringan saraf tiruan backpropagation pada salah satu indikator perubahan harga saham, yakni IHSG (Indeks Harga Saham Gabungan). Penelitian dilanjutkan dengan menghitung tingkat akurasi dan kehandalan metode yang telah diterapkan pada data IHSG. Pendekatan ini diharapkan dapat menjadi salah satu cara alternatif dalam meramalkan data IHSG sebagai salah satu indikator perubahan harga saham di Indonesia. Kata kunci—jaringan saraf tiruan, backpropagation, analisis data runtun waktu, soft computing, IHSG

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A Distributed Algorithm for Scalable Fuzzy Time Series

Green, Pervasive, and Cloud Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-19223-5_4 ◽

2019 ◽

pp. 42-56 ◽

Cited By ~ 1

Author(s):

Petrônio Cândido de Lima e Silva ◽

Patrícia de Oliveira e Lucas ◽

Frederico Gadelha Guimarães

Keyword(s):

Time Series ◽

Distributed Algorithm ◽

Fuzzy Time Series

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Fuzzy time series for predicting phenological stages of apple trees

Proceedings of the 36th Annual ACM Symposium on Applied Computing ◽

10.1145/3412841.3441968 ◽

2021 ◽

Author(s):

Tiago Boechel ◽

Lucas Micol Policarpo ◽

Gabriel de Oliveira Ramos ◽

Rodrigo da Rosa Righi

Keyword(s):

Time Series ◽

Fuzzy Time Series ◽

Phenological Stages ◽

Apple Trees

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Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

Renewable Energy ◽

10.1016/j.renene.2021.02.117 ◽

2021 ◽

Author(s):

Carlos A. Severiano ◽

Petrônio de Cândido de Lima e Silva ◽

Miri Weiss Cohen ◽

Frederico Gadelha Guimarães

Keyword(s):

Time Series ◽

Renewable Energy ◽

Energy Systems ◽

Fuzzy Time Series ◽

Renewable Energy Systems ◽

Spatio Temporal

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Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00872-w ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Tie Liang ◽

Qingyu Zhang ◽

Xiaoguang Liu ◽

Bin Dong ◽

Xiuling Liu ◽

...

Keyword(s):

Time Series ◽

Information Flow ◽

Gamma Band ◽

Motor Dysfunction ◽

Beta Band ◽

Short Time Series ◽

Information Interaction ◽

Stroke Group ◽

Short Time ◽

Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

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