Notes:

Time Series Prediction may also be known as “Time Series Forecasting”.

Wikipedia:

- Feature selection
- Hidden Markov model
- Statistical parsing
- Stochastic context-free grammar
- Stochastic grammar
- Stochastic simulation
- Time series

See also:

Natural Language & Time Series Prediction 2013 | Natural Language & Time Series Prediction 2014

**Fuzzy rule-based ensemble for time series prediction: Progresses with associations mining** M Burda, M Št?pni?ka, L Št?pni?ková – Strengthening Links Between Data …, 2015 – Springer … These are expressions of natural language that are based on the expressions of the basic trichotomy Small (Sm), Medium (Me), and Big (Bi). … Štepnicková, L., Štepnicka, M., Sikora, D.: Fuzzy rule-based ensemble with use linguistic associations mining for time series prediction. … Cited by 5 Related articles All 3 versions

**A hybrid fuzzy time series model based on granular computing for stock price forecasting** MY Chen, BT Chen – Information Sciences, 2015 – Elsevier … Rule representation means one fuzzy granule can be determined to generalize constraints and can be represented in natural language. Rule mining refers to the capacity of GrC to obtain more general rules by grouping attributes into granules. … Cited by 22 Related articles All 3 versions

**Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment** AK Nassirtoussi, S Aghabozorgi, TY Wah… – Expert Systems with …, 2015 – Elsevier … 2011. However, a search engine like the above is limited to extracting the available numeric data in the texts. Deciphering language by machine constitutes the complex field of natural language processing (NLP). From this … Cited by 18 Related articles All 6 versions

**Deep neural networks for ultra-short-term wind forecasting** M Dalto, J Matuško, M Vašak – Industrial Technology (ICIT), …, 2015 – ieeexplore.ieee.org … have reported state-of-the-art performance across fields such as object recognition [18], speech recognition [2], natural language processing [12], physiological affect model- ling [22], etc. There is still little literature on deep neural networks and time- series prediction. … Cited by 5 Related articles All 4 versions

**Early classification on multivariate time series** G He, Y Duan, R Peng, X Jing, T Qian, L Wang – Neurocomputing, 2015 – Elsevier Multivariate time series (MTS) classification is an important topic in time series data mining, and has attracted great interest in recent years. However, early. Cited by 12 Related articles All 3 versions

**ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster** E Volna, M Kotyrba, H Habiballa – The Scientific World Journal, 2015 – hindawi.com … rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series. 1. Background. Biometrical … Cited by 2 Related articles All 9 versions

**Tracking of moving target based on video motion nuclear algorithm** W Xiaojun, P Feng, W Eeihong – International Journal on …, 2015 – www-ist.massey.ac.nz … 190 obtained (KRR) and kernel canonical correlation analysis (KCCA). Kernel methods have been widely applied in many fields of text classification, face recognition, time series prediction. Nowadays, kernel method is the most commonly used is SVM, KPCA and KFDA, which … Cited by 4 Related articles All 4 versions

**Parameterizing time in electronic health record studies** G Hripcsak, DJ Albers, A Perotte – Journal of the American …, 2015 – jamia.oxfordjournals.org Skip to main content. OUP user menu. … Cited by 8 Related articles All 5 versions

**Predicting teenager’s future stress level from micro-blog** Y Li, J Huang, H Wang, L Feng – 2015 IEEE 28th International …, 2015 – ieeexplore.ieee.org … Natural language processing and machine learning techniques are usually applied sentiment analysis [19 … Second, we explore the use of multi-variant stochastic time series prediction techniques, meanwhile incorporating teenager’s stress coping features and upcoming events … Cited by 3 Related articles

**Learning with intelligent teacher: Similarity control and knowledge transfer** V Vapnik, R Izmailov – … Symposium on Statistical Learning and Data …, 2015 – Springer Page 1. Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer In memory of Alexey Chervonenkis Vladimir Vapnik1,2(B) and Rauf Izmailov3 1 Columbia University, New York, NY, USA vladimir.vapnik@gmail … Cited by 9 Related articles All 3 versions

**Time Series Classification with Linguistic Summaries** K Kaczmarek, O Hryniewicz – 16th world congress of the …, 2015 – atlantis-press.com … Linguistic summaries describe general facts about evolution of time series with quasi natural language. The formal definitions are as follows. … Protoforms of linguistic data summaries: towards more gen- eral natural-language-based data mining tools. … Cited by 1 Related articles All 2 versions

**Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting** CY Zhang, CLP Chen, M Gan… – IEEE Transactions on …, 2015 – ieeexplore.ieee.org … Recently, deep learning is well applied to the tasks like object recognition, speech recognition, and natural language process. … 1. For multiperiod time series prediction, it is to predict the value of xt+? by using the previous M data, where t is the index of the time series and ? … Cited by 6 Related articles

**Deep Learning** J Schmidhuber – Scholarpedia, 2015 – scholarpedia.org … synthesis (Fan et al., 2015; Zen & Sak, 2015, now available for Google Android), photo-real talking heads (Fan et al., Microsoft, 2015), syntactic parsing for natural language processing (Vinyals et … Time series prediction by using a connectionist network with internal delay lines. … Cited by 3 Related articles All 3 versions

**A Survey on Temporal Databases and Data mining** V Radhakrishna, PV Kumar, V Janaki – Proceedings of the The …, 2015 – dl.acm.org … The authors make use of natural language property where the word frequency count exhibits both the periodic and non-periodic behavior if the data is … on , vol., no., pp.559-564, 19-21 April 2012 [42] Policker, S.; Geva, AB, ”A new algorithm for time series prediction by temporal … Cited by 4 Related articles

**Visual predictions of traffic conditions** J Rhinelander, M Kallada, P Lingras – Canadian Conference on Artificial …, 2015 – Springer … However, instead of showing the textual natural language description for the category (or even worse, showing semantically obtuse values of the extracted image features … We implement a time-series prediction forecast of camera images using recent and current observations. … Cited by 1 Related articles

**A critical review of recurrent neural networks for sequence learning** ZC Lipton, J Berkowitz, C Elkan – arXiv preprint arXiv:1506.00019, 2015 – arxiv.org … In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translat- ing natural language, engaging in dialogue, and controlling a robot, often demand both … Cited by 37 Related articles All 10 versions

**Identifying outcome-discriminative dynamics in multivariate physiological cohort time series** S Nemati, RP Adams – … State Space Methods for Neural and …, 2015 – books.google.com … Although the standard use of RNNs has been for time series prediction (network output is the predicted input time series in the future) or … see Figure 12.1), describing the evolution of a set of J latent 1 A closely related problem considered in natural language processing under … Cited by 1 Related articles All 4 versions

**A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)** R Fahmy, H Zaher, AE Kandil – International Journal of …, 2015 – search.proquest.com … The Fuzzy logic is closer in spirit to human thinking and natural language than conventional logical systems are. … of statistical and neuro-fuzzy network models for forecasting the weather of Goztepe, Istanbul, Turkey, was presented by Tekta [7] A time series prediction model for … Cited by 3 Related articles All 3 versions

**Temporal embedding in convolutional neural networks for robust learning of abstract snippets** J Liu, K Zhao, B Kusy, J Wen, R Jurdak – arXiv preprint arXiv:1502.05113, 2015 – arxiv.org … Interestingly, such advantages of convolutional neural networks are present not only in vision tasks, but also in speech recognition [1, 8, 12] and natural language processing [6, 7]. Now we consider the periodical time-series prediction prob- lem for data such as daily traveling … Cited by 1 Related articles All 5 versions

**MD-ELM: originally mislabeled samples detection using OP-ELM model** A Akusok, D Veganzones, Y Miche, KM Björk… – Neurocomputing, 2015 – Elsevier This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c-class classification problem (dataset). The methodology r. Cited by 3 Related articles All 4 versions

**Maintenance of belt conveyors using an expert system based on fuzzy logic** D Mazurkiewicz – Archives of Civil and Mechanical Engineering, 2015 – Elsevier … value. Taking advantage of this formula, we can use fuzzy logic to assign to every value an accurate number that indicates agreement between this value and its description in a natural language [25], [26], [27] and [28]. With … Cited by 5 Related articles All 5 versions

**Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes** N Peek, C Combi, R Marin, R Bellazzi – Artificial intelligence in medicine, 2015 – Elsevier … Conclusions. There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. … Cited by 6 Related articles All 6 versions

**Predicting daily river runoff by deep belief networks** TN Cao, HN Duong, HV Tran, V Snasel – researchgate.net … Particularly, DNNs have been used in object recognition, speech recognition, natural language processing, physiological affect modeling and so on [16]. … The main reason is that there are several simpler methods successfully solving the problem of time series prediction such as … Related articles

**Feature Extraction and Forecasting Model of Gas Vehicle Energy Consumption Based on Time Series** Z Yan, J Yaming, X Huajian, Z Wenxiao, W Haiwei… – 2015 – ijasm.org … As the modeling tool of a kind of time series, HMM has been widely applied in the field of speech recognition, ECG analysis, stock price time series prediction and etc. … His interests include: machine learning, natural language processing and internet of things. … Related articles

**Approximation Neural Network for Phoneme Synthesis** M Crisan – Proceedings of the 3rd International Conference on …, 2015 – Springer … The main difficulty resides in dealing with the nonlinear character of natural language phenomenon. … The approximation or interpolation networks, also known as radial basis function (RBF) networks, offer a series of advantages for time-series prediction due to the nature of the … Related articles All 3 versions

[BOOK] **Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization** P Melin, O Castillo, J Kacprzyk – 2015 – Springer … Patricia Melin and German Prado-Arechiga Optimization of Ensemble Neural Networks with Fuzzy Integration Using the Particle Swarm Algorithm for Time Series Prediction….. … Analysis of Some Database Schemas Used to Evaluate Natural Language Interfaces to Databases … All 3 versions

**Time Series Forecasting Based on Cloud Process Neural Network** B Wang, S Xu, X Yu, P Li – International Journal of Computational …, 2015 – Taylor & Francis … The cloud model reflects not only the concepts’ uncertainty in natural language, but also the relevance between randomness and fuzziness, and has already been successfully applied in uncertainty reasoning, intelligent control, decision support, spatial clustering, and system … Related articles All 2 versions

**Link value and event-result prediction for sequence behavior in social networks** LIU BingQuan, LIU Feng, W XiaoLong… – SCIENTIA SINICA …, 2015 – scichina.com … Cognitive Sci, 1990, 14: 179-211. [22] Connor JT, Martin RD, Atlas L E. Recurrent neural networks and robust time series prediction. … 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing …

**The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models** JB Nascimento, M Cristo – Proceedings of the 21st Brazilian Symposium …, 2015 – dl.acm.org … More recently, many studies on Natural Language Pro- cessing have favored the adoption of conceptually distributed representations of language events such … We study the impact of our proposal on the performance of some classical methods for time-series prediction that take … Related articles

**Feature Selection** AM De Silva, PHW Leong – … Feature Generation for Time-Series Prediction, 2015 – Springer … In time-series prediction tasks on stationary data, tests such as a Granger causality test can be used as a tool to … Randomized search: In practical applications, the feature space may contain thousands of features, eg bioinformatics, text, natural language processing applications … Related articles

**Short term power load prediction with knowledge transfer** Y Zhang, G Luo – Information Systems, 2015 – Elsevier … Transfer learning methods have been successfully used in the problems such as natural language processing [23] and wireless localization [24 … t is the independent white Gaussian noise with mean value ? e =0 ? e = 0 and variance ? n 2 . For time series prediction problems, the … Cited by 1 Related articles All 3 versions

**On regression methods based on linguistic descriptions** J Kupka, P Rusnok – Fuzzy Systems (FUZZ-IEEE), 2015 IEEE …, 2015 – ieeexplore.ieee.org … In this paper we provide a short analysis of a part of a method that was successfully used in time series prediction (eg [15], [16]) and decision making … Additionally, the method from [15] provided linguistic, ie interpretable in natural language, description of behavior of time series. … Cited by 2 Related articles

**An In-Depth Context-Awareness Framework for Pervasive Video Cloud** W Zhang, P Duan, L Chen – … and 2015 IEEE 12th Intl Conf on …, 2015 – ieeexplore.ieee.org … For example, using time domain features classic CNN was extended to three dimensions in video in order to conduct action recognition [8]. CNN can be used to understand an image with natural language [9]. These … Time series prediction competi- tion: The cats benchmark. …

**Indeterminacy Reduction in Agent Communication Using a Semantic Lnaguage** H PAGGI, M COCHEZ – users.jyu.fi … truth and, is then naturally related to the fuzzy sets theory and its concept of membership function [15, 18]; it is also associated with how a phenomenon is defined (and not with its occurrence) and it is typical of natural language. … 1.2.4 Neural networks and time series prediction … Related articles

**A Controller Design Researh Based on the Cloud Model** F Jie, J Wang – Indonesian Journal of Electrical Engineering and …, 2015 – iaesjournal.com … to give a precise mathematical model of controlled object, it is based solely on a person’s feelings and logic, human qualitative control experience is expressed in natural language, these are … Application of uncertainty reasoning based on cloud model in time series prediction. … Related articles

**Classification of EEG with Recurrent Neural Networks** AS Greaves – cs224d.stanford.edu … one element in the sequence to the next must be different, to some impactful degree, in EEG from Natural Language Processing. … and T. McGinnity, ”Extracting features for a brain-computer interface b self-organizing fuzzy neural network- based time series prediction,” in 26th … Related articles All 4 versions

**General Information of IJAIASD** Z Vale – sersc.org … learning • Multiple hypothesis testing • Multisensor data fusion using neural and fuzzy techniques • Natural language processing • Nature … learning theory • Stochastic optimization • Supervised and unsupervised classification of web data • Time series prediction • Topics on … All 2 versions

**Grammar Based Feature Generation** AM De Silva, PHW Leong – … Feature Generation for Time-Series Prediction, 2015 – Springer … Context-free grammars Feature generation framework Wavelet based time-series prediction Technical indicators Heuristic feature pruning Hybrid feature selection and … Such grammars are used in linguistics to describe sentence structure and words of a natural language and in … Related articles

**Emission Allowances Prices Predictions for the Purposes of Managerial Decision Making** J Zimmermannova, F Hunka – International Business Management, 2015 – docsdrive.com … a frame of time series prediction lies in the learning of these rules from the series and then their application to the future (predicted) members of the series (Dvooak et all, 2003). Fuzzy IF-THEN rules can be understood as a specific conditional sentence of natural language of the … Related articles All 2 versions

**Discriminative Model for Google Host Load Prediction with Rich Feature Set** P Huang, D Ye, Z Fan, P Huang… – Cluster, Cloud and Grid …, 2015 – ieeexplore.ieee.org … Natural language processing, computer vision and other machine learning research areas have increasingly profited from discriminative approaches [8 … The discriminative SVM model in this paper is evaluated by comparing with four time series prediction methods, and the … Related articles All 2 versions

**An analysis of heterogeneous ensembles at predicting stock prices of Brazilian power companies** DC Furlaneto, RCFH dos Santos, CPJ das Amricas – inf.ufpr.br … have achieved state-of-the-art results on a number of problems on different fields, such as natural language, image analysis … Given the success they achieved in other problems, machine learning techniques have been used on financial time series prediction extensively, with a … Related articles All 2 versions

**Stacked Denoising Auto-Encoders for Short-Term Time Series Forecasting** P Romeu, F Zamora-Martínez… – Artificial Neural …, 2015 – Springer … Deep learning is being depth studied in computer vision and natural language processing tasks [28, 36, 9], allowing to train complex classifiers which … is computed comparing tar- get values for the time series st+1,st+2,…,st+H and its corresponding time series prediction ˆst+1, ˆst … Related articles All 3 versions

**Complex Fuzzy Sets and Complex Fuzzy Logic an Overview of Theory and Applications** DE Tamir, ND Rishe, A Kandel – Fifty Years of Fuzzy Logic and its …, 2015 – Springer … Namely, a linguistic variable is a variable whose domain of values is comprised of formal or natural language words [3]. Generally, a linguistic variable is related to a fuzzy set such as \( \{ very\;young\;male,\;young\;male,\;old\;male,\;very\;old\;male\} \) and can get any value from … Related articles All 6 versions

**Indeterminacy Reduction in Agent Communication Using a Semantic Language** H Paggi, M Cochez – 2015 – jyx.jyu.fi … of truth and is then naturally related to the fuzzy sets theory and its concept of membership function [15, 18]; it is also associated with how a phenomenon is defined (and not with its occurrence) and is typical of natural language. … 1.2.4 Neural networks and time series prediction … Related articles

**A comparative study of hybrid artificial neural network models for one-day stock price prediction** J Alam, J Ljungehed – 2015 – diva-portal.org … Some activities that computers with ar- tificial intelligence are designed for are learning, reasoning, planning, perception and natural language processing [19]. … In time series prediction, these tools are used in the pre-processing of input data. … Related articles

**Temporal Kernel Descriptors for Learning with Time-sensitive Patterns** D Sahoo, A Sharma, SCH Hoi, P Zhao – doyensahoo.com … prediction [5]. These methods address a slightly different problem and do not exploit timestamp information to improve predictability. Some efforts have been made to use tree-kernels for linking events to timestamps [24, 14], but here the focus is primarily on natural language … Cited by 1 Related articles

**Key indicators of rice production and consumption, correlation between them and supply-demand prediction** S Sharma, SV Patil – International Journal of Productivity and …, 2015 – emeraldinsight.com … Fuzzy logic is based on the natural language of human communication (www.mathworks.in/help/ toolbox/fuzzy/fp72.html). Fuzzy logic is useful for predicting responses of nonlinear systems with arbitrary inputs that would be difficult or impossible to model mathematically. … Related articles

**Proposed system for predicting Buy, Hold and Sell recommendations for a publicly listed Philippine company using computational intelligence** R Morta, E Dadios – TENCON 2015-2015 IEEE Region 10 …, 2015 – ieeexplore.ieee.org … of AI is based on the Turing test which is as follows: a human judge engages in a natural language conversation with … Gestel, TV, Suykens, JAK, Baestaens, DE, Lambrechts, A., Lanckriet, G., Vandaele, B., Moor, BD, Vandewalle, J., Financial Time Series Prediction Using Least … Related articles

**Hybrid Nsga-Ii Optimization For Improving The Three-Term Bp Network For Multiclass Classification Problems**. AO Ibrahim, SM Shamsuddin… – Journal of Information & …, 2015 – search.ebscohost.com … was made to optimize the training and the topology of the recurrent neural network (RNN) simultaneously in time-series prediction problems. … In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1–8. Crammer, K., Dekel, O … Related articles

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**Stock Market Prediction using Social Media Analysis** O Bahceci, O Alsing – 2015 – diva-portal.org … The prediction of stock markets is considered to be a challenging task of financial time series prediction. … Many of these approaches are spawned from Natural Language Processing or make use of data mining methodologies such as N-grams (Arafat, Ahsan Habib, and Hossain … Related articles

**Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil** A Ten, TB Ludermir – 2015 Brazilian Conference on …, 2015 – ieeexplore.ieee.org … Deep Learning can be applied in various problems, both in academy and in industry, ie in speech recognition and signal processing [14], object recognition [15], natural language processing [16 … In this experiment, N is given by 1, because it is a problem of time series prediction. … Related articles All 3 versions

**An Opinion Predictor Using Recurrent Neural Networks** SP Polisetty, TV Rao – search.proquest.com … series prediction. Neural Networks, IEEE Transactions. 1994;5(2):240-254. [15] Venugopal V, Baets W. Neural networks and their applications in marketing management. Journal of Systems Management. 1994;45:16-16. [16] Duh Kevin. Deep Learning for Natural Language … Related articles All 2 versions

**Event and state detection in time series by genetic programming** F Xie – 2015 – researchbank.rmit.edu.au Page 1. Event and State Detection in Time Series by Genetic Programming A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Feng Xie Master of Computer Science School of Computer Science and Information Technology … Related articles All 2 versions

**Fuzzy rule base ensemble generated from data by linguistic associations mining** M Štepnicka, M Burda, L Štepnicková – Fuzzy Sets and Systems, 2015 – researchgate.net … Abstract As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always … of fuzzy/linguistic IF–THEN rules are evaluative linguistic expressions [23], ie, special expressions of a natural language that are … Cited by 3 Related articles

**The posterity of Zadeh’s 50-year-old paper** JC Bezdek, D Dubois, H Prade – Fuzzy Systems (FUZZ-IEEE), …, 2015 – ieeexplore.ieee.org … [33] Kacprzyk, J., Zadrozny S., Linguistic database summaries and their protoforms: towards natural language based knowledge … [36] Kasabov N. and Qun Song, “DENFIS: Dynamic Evolving Neural- Fuzzy Inference System and its application for time-series prediction,” TFS, 10(2 … Related articles All 8 versions

**The NIST data science initiative** BJ Dorr, CS Greenberg, P Fontana… – Data Science and …, 2015 – ieeexplore.ieee.org … between variables. In our pilot traffic flow prediction challenge, we wish to predict traffic speed using covariates including flow volume, percentage occupancy, and training sets of past multivariate time series. Prediction. Prediction … Cited by 2 Related articles

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**A Self Adaptive Incremental Learning Fuzzy Neural Network Based on the Influence of a Fuzzy Rule** H Rong, X Ye, X Xiang – 2015 International Conference on …, 2015 – ieeexplore.ieee.org … FNN combine the human inference style and natural language description of fuzzy systems with the learning and parallel processing of neural networks … Section 4, the algorithm is applied to nonlinear system identification, the Mackey-Glass chaotic time-series prediction problem … Related articles

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**Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model** J Geng, ML Huang, MW Li, WC Hong – Neurocomputing, 2015 – Elsevier … 2.2. Chaotic cloud simulated annealing algorithm. 2.2.1. Basic concept of cloud theory. The cloud theory is such kind of model that includes the transformation process of uncertainty between qualitative and quantitative representations by natural language [28]. … Cited by 7 Related articles

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**Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor** R Harikumar – International Journal of Imaging Systems and …, 2015 – Wiley Online Library … The output of the RBFNN is a linear combination (weighted sum) of the radial basis function calculated by the kernel units. RBF networks are very popular for function approximation, curve fitting, Time Series prediction, and control problems. … Cited by 20 Related articles All 3 versions

[BOOK] **Logic in the Theory and Practice of Lawmaking** M Araszkiewicz, K Pleszka – 2015 – Springer … 137 Giovanni Battista Ratti 6 Open Texture in Law, Legal Certainty and Logical Analysis of Natural Language….. … artificial immune systems, signal processing, event detection, similarity analysis, legal text processing, time series prediction, qualitative data … Cited by 1 Related articles All 2 versions

**Potentials of Cloud Model and Seeker Optimization Algorithm: an Approach to Improve Non-Redundant Fuzzy Association Rule Mining** SM Darwish, RAA Salam… – Journal of Next …, 2015 – search.proquest.com … Cloud model takes advantage of human natural language, and is able to search for qualitative concepts described by natural language to generalize a given set of quantitative data [6-9]. Since its invention, cloud model has been widely used in many applications [10]. … Related articles

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**Machine Learning Techniques in Plant Biology** K Osama, BN Mishra, P Somvanshi – PlantOmics: The Omics of Plant …, 2015 – Springer … Artificial neural networks are used for problems like function approximation, regression analysis, time series prediction, classification, pattern recognition, decision making, data processing, filtering, clustering, etc. Structure of Artificial Neural Network. … Related articles

**Artificial immune system based web page classification** A Onan – Software Engineering in Intelligent Systems, 2015 – Springer … the methods of several disciplines, such as data mining, information retrieval, machine learning and natural language processing. … Negative selection based algorithms have been successfully applied for anomaly detection, time series prediction, image segmentation and hard … Cited by 2 Related articles All 3 versions

**Forecasting of a hydropower plant energy production** K Dmitrieva – 2015 – brage.bibsys.no … Keywords: time series, prediction, forecasting, machine learning, regression, ARIMA, R, Matlab, SVM, Artificial Neural Networks, energy production, hydropower plant i Page 4. Page 5. Acknowledgments … 9 2.5 Time Series Prediction . . . . . … Related articles All 2 versions

**Analyzing Analytics** R Bordawekar, B Blainey, R Puri – Synthesis Lectures on …, 2015 – morganclaypool.com … Key Functional Characteristics Google, Bing search Web crawling, Link analysis of the web graph, Result ranking, Indexing Multi-media data Netflix and Pandora Analyzing structured and unstructured data, Recommendation Watson Natural language processing, Processing … Cited by 3 Related articles All 9 versions

**Weighted tanimoto extreme learning machine with case study in drug discovery** WM Czarnecki – IEEE Computational Intelligence Magazine, 2015 – ieeexplore.ieee.org Page 1. 1556-603x/15©2015ieee august 2015 | ieee Computational intelligenCe magazine 19 Abstract–Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific … Cited by 9 Related articles All 3 versions

**Data Acquisition for Real-time Decision-making under Freshness Constraints** S Hu, S Yao, H Jin, Y Zhao, Y Hu, X Liu… – Real-Time Systems …, 2015 – ieeexplore.ieee.org Page 1. Data Acquisition for Real-time Decision-making under Freshness Constraints Shaohan Hu ? , Shuochao Yao ? , Haiming Jin ? , Yiran Zhao ? , Yitao Hu † , Xiaochen Liu † , Nooreddin Naghibolhosseini ‡ , Shen Li ? , Akash Kapoor ? , William Dron § , Lu Su ¶ , … Cited by 4 Related articles All 5 versions

**Neuro-Fuzzy System Technique for Obstructed Avoidance of Several Mobile Robot** H WONGSUWARN – researchgate.net … drawn from a natural language, in which the boundaries of perceived classes are fuzzy … 2. NEURAL NETWORK AND NEUROFUZZY APPROACHES FOR THE TIME SERIES PREDICTION 2.1 Neurofuzzy System (NFs) for Modeling and Identification Both neural networks and the …

**Artificial Neural Network Models** P Tino, L Benuskova, A Sperduti – Citeseer Page 1. Artificial Neu 455 Pa rt D|27.1 27. Artificial Neural Network Models Peter Tino, Lubica Benuskova, Alessandro Sperduti We outline the main models and developments in the broad field of artificial neural networks (ANN). … Related articles All 2 versions

**ConSent: Context-based sentiment analysis** G Katz, N Ofek, B Shapira – Knowledge-Based Systems, 2015 – Elsevier … and identify problems at their early stages. Sentiment analysis is considered a challenging natural language processing (NLP) problem [8], and particularly so for Twitter and transcribed text. Twitter is difficult to analyze due to … Cited by 13 Related articles All 3 versions

**A minimal architecture for general cognition** MS Gashler, Z Kindle, MR Smith – 2015 International Joint …, 2015 – ieeexplore.ieee.org … not yet been achieved as predicted. Artificial intelligence has been successfully applied in several specific applications such as natural language processing and computer vision. However, these domains are now subfields … Related articles All 6 versions

**Air quality prediction by machine learning methods** H Peng – 2015 – open.library.ubc.ca Learning, knowledge, research, insight: welcome to the world of UBC Library, the second-largest academic research library in Canada. All 2 versions

**Modality of Adaptive neuro-fuzzy classifier for acoustic signal-based traffic density state estimation employing linguistic hedges for feature selection** P Borkar, MV Sarode, LG Malik – International Journal of Fuzzy Systems, 2015 – Springer … The Neuro-fuzzy classifiers define the class distributions and show the input–output relations, whereas the fuzzy systems describe the systems using natural language. Neural networks are employed for training the system parameters in neuro-fuzzy applications. … Cited by 3 Related articles

**Equivalence results between feedforward and recurrent neural networks for sequences** A Sperduti – … of the 24th International Conference on …, 2015 – pdfs.semanticscholar.org … 1 Introduction Learning on sequential data has always been a hot topic since many are the application domains where this skill could be applied, eg natural language processing, bioinformat- ics, video surveillance, time series prediction, robotics, etc. … Cited by 2 Related articles All 5 versions

**Loads management** S Feilmeier – 2015 – sf.fenecon.de … Learning ….. 29 III.2.1 Artificial Neural Networks ….. 29 III.2.2 Time-Series prediction ….. 32 III.2.3 Implementation of generic predictors ….. 33 … Related articles

[BOOK] **Machine learning: an algorithmic perspective** S Marsland – 2015 – books.google.com … and KamelMekhnacha UTILITY-BASED LEARNING FROM DATA Craig Friedman and Sven Sandow HANDBOOK OF NATURAL LANGUAGE PROCESSING, SECOND … with the MLP 92 4.4.3 A Classification Example: The Iris Dataset 93 4.4.4 Time-Series Prediction 95 4.4.5 … Cited by 448 Related articles All 7 versions

**An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy** D Opresnik, M Fiasché, M Taisch, M Hirsch – Information Technology and …, 2015 – Springer … necessary in the case of strategy definition, because the premises are expressed as propositions in a natural language, which is … can learn in an incrementally adaptive mode any dataset, regardless of the problem (function approximation, time-series prediction, classification, etc … Cited by 5 Related articles

**Web Search Using Summarization on Clustered Web Documents Retrieved by User Queries** R Qumsiyeh, YK Ng – … on Web Intelligence and Intelligent Agent …, 2015 – ieeexplore.ieee.org … in) S (as discussed in Section II-A4), (ii) must be coherent and comprehensible, which can be achieved using natural language processing to … Since exponential average is extensively used in time-series prediction, QSum uses the decay rate formula in computing TD(S), which … Related articles All 2 versions

**Recommending blog articles based on popular event trend analysis** DR Liu, H Omar, CH Liou, HC Chi, CH Hsu – Information Sciences, 2015 – Elsevier … Glance et al. [20] apply Natural Language Processing (NLP) algorithm … Time series prediction [9] analyzes a sequence of data points in order to extract meaningful statistical information and construct a suitable model to predict future values based on previously observed values. … Cited by 9 Related articles All 6 versions

**Self-organization and missing values in SOM and GTM** T Vatanen, M Osmala, T Raiko, K Lagus, M Sysi-Aho… – Neurocomputing, 2015 – Elsevier In this paper, we study fundamental properties of the Self-Organizing Map (SOM) and the Generative Topographic Mapping (GTM), ramifications of the initializatio. Cited by 12 Related articles All 3 versions

**Tensor factorization in civil infrastructure systems** OA Adarkwa – 2015 – udspace.udel.edu … deficiencies and functional obsolescence of bridges under different classifications of traffic volumes. The third analysis uses a time series prediction approach with the tensor factorization to predict future structural deficiencies of bridges in states. The … Cited by 2 Related articles All 3 versions

**Predicting Stock Markets with Neural Networks** T Aamodt – 2015 – duo.uio.no … in [45] uses natural language models together with a technique called machine learning to infer how a given news story will affect a related stock in the immediate future … In statistics, time series prediction is a well-known problem, accompanied by numerous mathematical models … Related articles

[BOOK] **Introduction to Statistical Machine Learning** M Sugiyama – 2015 – books.google.com … 365 32.2 Bootstrap Confidence Estimation….. 367 32.3 Applications ….. 368 32.3.1 Time-series Prediction….. 368 32.3.2 Tuning Parameter Optimization ….. … Cited by 1 All 2 versions

[BOOK] **Advances in Artificial Intelligence and Soft Computing** G Sidorov, SN Galicia-Haro – 2015 – Springer … The first volume, Advances in Artificial Intelligence and Soft Computing, contains 46 papers structured into eight sections: – Invited Paper – Natural Language Processing – Logic and Multi-agent Systems – Bioinspired Algorithms – Neural Networks – Evolutionary Algorithms … Related articles All 2 versions

**A unified model for context-based behavioural modelling and classification** JJ Dabrowski, JP de Villiers – Expert Systems with Applications, 2015 – Elsevier … A general framework for applying machine learning methods for time series prediction is presented in Wu and Lee (2015 … of context-based information fusion include human activity classification (Xu, Chang, Chien, Kaiser, & Pottie, 2014), natural language processing (Steinberg … Cited by 2 Related articles All 6 versions

**An improved cuckoo search based extreme learning machine for medical data classification** P Mohapatra, S Chakravarty, PK Dash – Swarm and Evolutionary …, 2015 – Elsevier … like optical character recognition [1], text and image classification [2], machine vision [3], fraud detection [4], natural language processing [5 … been done using ELM in the field of designing filters in image applications [44], sales forecasting [45], time series prediction [46], power … Cited by 12 Related articles All 2 versions

**IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis** HT Chang, N Mishra, CC Lin – PloS one, 2015 – journals.plos.org … A knowledge domain inherits the features of a natural language processing model, conceptual dependency model, and logical inference model and predicates a model that can be used to construct a knowledge-based information system, expert system, or rule-based system. … Cited by 4 Related articles All 10 versions

**Machine learning models for some learning analytics issues in massive open online courses** F Mi – 2015 – cse.ust.hk … features are captured continuously for each student over a period of time, dropout pre- diction is essentially a time series prediction problem. By regarding dropout prediction … can be seen as a sequence labeling problem [1] or a time series prediction problem [25]. 3 Page 16. … Related articles All 3 versions

**Modeling and Evaluating Information Diffusion for Spam Detection in Micro-blogging Networks** K Chen, P Zhu, L Chen, Y Xiong – KSII TRANSACTIONS ON INTERNET …, 2015 – itiis.net … For example, the terms “discount”, “sold-out” and “on sale” are usually used in advertisement spams. Zhang [21] studies the utility of reviews based on natural language features. Jindal [22] studied the spam product reviews of Amazon.com. … Related articles All 4 versions

**A pipeline for extracting and deduplicating domain-specific knowledge bases** M Kejriwal, Q Liu, F Jacob… – Big Data (Big Data), 2015 …, 2015 – ieeexplore.ieee.org … paper. Deduplication is a data management problem that, al- though initially applied to tabular databases, has since emerged in structured, semi-structured and unstructured (that is, natural language) applications [16], [11]. … Cited by 4 Related articles All 2 versions

**Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighbors** CH Chen – Information Sciences, 2015 – Elsevier … The selection of salient features is an important issue in cluster analysis and is relevant for topics such as image representation [24], time-series prediction [45], machine learning [41] and natural language processing [5]. Typically, the use of a large number of features to … Cited by 3 Related articles All 3 versions

**Machine Learning using Cellular Automata based Feature Expansion and Reservoir Computing** O Yilmaz – Journal of Cellular Automata, 2015 – ozguryilmazresearch.net … systems [25]. They are particularly appealing for problems that require remembering long-range statistical rela- tionships such as speech, natural language processing, video processing and financial data analysis. Despite their … Cited by 3 Related articles All 2 versions

[BOOK] **Advances in Artificial Intelligence and Its Applications: 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Cuernavaca, Morelos, …** OP Lagunas, OH Alcántara, GA Figueroa – 2015 – books.google.com … The first volume, Advances in Artificial Intelligence and Soft Computing, contains 46 papers structured into eight sections: – Invited Paper – Natural Language Processing – Logic and Multi-agent Systems – Bioinspired Algorithms – Neural Networks – Evolutionary Algorithms … Related articles

[BOOK] **Three Domain Modelling and Uncertainty Analysis** A Mirakyan, R De Guio – 2015 – Springer Page 1. Energy Systems Atom Mirakyan Roland De Guio Three Domain Modelling and Uncertainty Analysis Applications in Long Range Infrastructure Planning Page 2. Energy Systems Series editor Panos M. Pardalos, Gainesville, USA Page 3. … Related articles All 2 versions

**Statistical learning for decision making: interpretability, uncertainty, and inference** B Letham – 2015 – dspace.mit.edu … we apply it to a medical dataset consisting of individual patient histories. The sequential event prediction problems we consider here are different from time- series prediction problems, that one might handle with a Markov chain. For instance, 26 Page 27. … Related articles

**Free from Publisher A cognitive adopted framework for IoT big-data management and knowledge discovery prospective** N Mishra, CC Lin, HT Chang – International Journal of Distributed Sensor …, 2015 – dl.acm.org … An IoT knowledge base framework inherits the features of natural language pro- cessing model, conceptual dependency model, logical infer- ence model, and predicates model to construct a knowledge base information system (KBIS). … Cited by 8 Related articles All 7 versions

**Deep Convolutional Neural Networks for Smile Recognition** PO Glauner – arXiv preprint arXiv:1508.06535, 2015 – arxiv.org … Overall, significant progress in machine learning and pattern recognition has been made in natural language processing, computer vision and … LSTMs have been reported to outperform regular RNNs and Hidden Markov Models in classification and time series prediction tasks. … Cited by 3 Related articles All 3 versions

**Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges** JR Prasad, U Kulkarni – International Journal of Machine Learning and …, 2015 – Springer … systems [8–12]. The neuro-fuzzy systems define the class distributions and show the input–output relations [8, 13–15], whereas the fuzzy systems employ natural language for developing fuzzy rules. Neural networks are employed … Cited by 1 Related articles All 2 versions

**Recurrent Neural Networks For Polyphonic Sound Event Detection** G PARASCANDOLO – 2015 – dspace.cc.tut.fi … Neural networks are employed in a wide variety of tasks in machine learning, such as pattern classification, regression and time-series prediction. Neurons, layers and activation functions are common components of most NNs despite the variety … Cited by 1 Related articles

**Big data analytics for emaintenance: Modeling of high-dimensional data streams** L Zhang – 2015 – diva-portal.org … Factor LOS Local Outlier Score MIS Management Information System MPP Massively Parallel Processing MSPC Multivariate Statistical Process Control MTBD Mean Time Between Degradation MTBF Mean Time Between Failure NLP Natural Language Processing OLAP … Cited by 1

**Evolving granular fuzzy model-based control of nonlinear dynamic systems** D Leite, RM Palhares, VCS Campos… – IEEE Transactions on …, 2015 – ieeexplore.ieee.org … values over time periods [28]. In general, inaccurate measurements and perception-based information are granular by the very nature of the measuring instruments and subjectivity of the natural language. Fuzzy set theory, as … Cited by 6 Related articles All 5 versions

**Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing** O Yilmaz – arXiv preprint arXiv:1503.00851, 2015 – arxiv.org … RNNs are known to be Turing complete computational tools [1] and universal ap- proximators of dynamical systems [2]. They are especially appealing for problems that require remembering long-range statistical relationships such as speech, natural language processing, video … Cited by 4 Related articles All 7 versions

**Machine Learning in a Tiny Nutshell** H Jaeger – 2015 – Citeseer … this vocabulary contains 10,000 words (that would be a typical size for today’s ML systems that handle natural language texts; I … often implicit) of the data distribution which lends itself to efficient application purposes, like pattern classification, time series prediction, motor control … Related articles All 2 versions

**Mining Electronic Health Records (EHR): A Survey** P Yadav, M Steinbach, V Kumar, G Simon – 2015 – cs.umn.edu … exposures and lifestyle data all reside in clinical notes. Natural language processing (NLP) tools and techniques have been widely used to extract knowledge from EHR data. Clinical notes such as admission, treatment and discharge … Related articles All 2 versions

**Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications** R Uppala – 2015 – rave.ohiolink.edu … The field of machine learning has achieved remarkable progress in many classes of problems such as pattern recognition, natural language processing and time series prediction. For realistic tasks such algorithms perform significantly better when massive computation power … Related articles All 2 versions

**Comparative Study of Sentiment Detection Techniques for Business Analytics** H Avery – 2015 – etd.auburn.edu Page 1. Comparative Study of Sentiment Detection Techniques for Business Analytics by Heather Avery A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree … Related articles All 3 versions

**Multiclass semisupervised learning based upon kernel spectral clustering** S Mehrkanoon, C Alzate, R Mall… – IEEE transactions on …, 2015 – ieeexplore.ieee.org Page 1. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Multiclass Semisupervised Learning Based Upon … Cited by 13 Related articles All 11 versions

**Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification** Y Zhou, Y Wei – 2015 – ieeexplore.ieee.org … A. Deep Learning Deep learning, inspired by the mechanism of human vision, recently attracted more and more attentions due to its good performance in many fields such as speech recognition, com- puter vision, and natural language processing [40]–[42]. … Cited by 7 Related articles All 3 versions