Neural Network Based Time Series Forecasting Of Student Enrollment With Exponential Smoothing Baseline And Statistical Analysis Of Performance

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Neural Network-based Time Series Forecasting of Student Enrollment with Exponential Smoothing Baseline and Statistical Analysis of Performance

Author : Friday James
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (132 download)

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Book Synopsis Neural Network-based Time Series Forecasting of Student Enrollment with Exponential Smoothing Baseline and Statistical Analysis of Performance by : Friday James

Download or read book Neural Network-based Time Series Forecasting of Student Enrollment with Exponential Smoothing Baseline and Statistical Analysis of Performance written by Friday James and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The sustainability of educational institutions generally depends largely on strategic planning, both in terms of optimal allocation of resources/manpower and budgeting for financial aids/scholarships to incoming students. Hence, forecasting of student enrollment plays a vital role in making crucial decisions based on previous time-bound records. This work demonstrates the power of neural network-based time series forecast over a traditional time series model and recommends the better network architecture between deep and shallow neural networks based on 25-year historical records of student enrollment in Programming Fundamentals from 1995 - 2020 at Kansas State University, Manhattan Campus. The study reveals that Vanilla Long Short-Term Memory (LSTM) model performs better than the deep neural network with Root Mean Square Errors (RMSE) of 0.11 and 0.24 respectively - both of which produced better results than the Single Exponential Smoothing baseline having a RMSE of 0.27. The study also carries out a statistical analysis of 5-year student performance based on weekly Labs, Projects and Mid-Terms using Analysis of Variance (ANOVA). The result shows the existence of differences in the yearly average performance of students. Post Hoc Tukey's pairwise multiple comparison tests reveals consistency in performance up to the period of the semester where possible dropouts would have occurred. Students' delay in tackling challenging projects also accounts for the significant differences in the mean scores.

TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 :
Total Pages : 283 pages
Book Rating : 4./5 ( download)

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Book Synopsis TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB by : Cesar Perez Lopez

Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB

Handbook of Operations Research and Management Science in Higher Education

Author : Zilla Sinuany-Stern
Publisher : Springer Nature
ISBN 13 : 303074051X
Total Pages : 529 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Handbook of Operations Research and Management Science in Higher Education by : Zilla Sinuany-Stern

Download or read book Handbook of Operations Research and Management Science in Higher Education written by Zilla Sinuany-Stern and published by Springer Nature. This book was released on 2021-09-09 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included.

Advanced Analytics and Learning on Temporal Data

Author : Vincent Lemaire
Publisher : Springer Nature
ISBN 13 : 3030914453
Total Pages : 202 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Advanced Analytics and Learning on Temporal Data by : Vincent Lemaire

Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2021-12-02 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.

State-of-the-art Deep Learning for Multi-product Intermittent Time Series Forecasting

Author : Ronish Samir Raval
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (127 download)

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Book Synopsis State-of-the-art Deep Learning for Multi-product Intermittent Time Series Forecasting by : Ronish Samir Raval

Download or read book State-of-the-art Deep Learning for Multi-product Intermittent Time Series Forecasting written by Ronish Samir Raval and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is gaining traction and considerable attention due to the state-of-the-art results obtained in computer vision, object detection, natural language processing, sequential analysis, and multiple other domains. Study of literature reveals that time series analysis is a good candidate for modeling using deep learning techniques. Time series analysis has applications from finance to supply chain domains and proves to be critical in driving organizations' profit and strategic growth. In a retail setting, product demand forecasting helps in minimizing inventory, optimizing service levels, and maximizing revenue. When dealing with demand forecasting, a much complex branch of intermittent demand profiles arises. When forecasting time series, the standard option comes down to statistical learning methods such as ARIMA, exponential smoothing, and several other models. However, in case of intermittency in demand and forecasting multiple time series at once, statistical learning methods fail to provide a high level of accuracy and can sometimes become computationally expensive as well. Deep learning algorithms enter the fray, as they can be applied to tackle the problem of forecasting intermittent sales while solving the problem in a computationally frugal manner. The study focuses on solving these two problems using a state-of-the-art based approach. It helps us answer the questions of -- How to implement neural networks in a value-add manner? And which models and architectures work best in our time series prediction problem with similar real-world applications? The study reveals that recurrent and convolutional architectures exhibit versatility and value in solving this problem, helping us understand the deep learning models and their application architectures in real-world scenarios. In this thesis, we have tried to answer these two important questions. The data was obtained from Kaggle for the M5 forecasting competition. The dataset relates to the daily Walmart sales of 3,000 products ranging across 10 stores. The data comprises of 3 different categories and 7 sub-categories, making it a multi-time series forecasting problem. We have applied the methods of statistical learning and deep learning to solve this problem. Statistical models of naïve method, moving average, ARIMA, Croston forecasting have been implemented. In deep learning, we initially use the deep feed-forward neural network to forecast the sales. Then recurrent architectures of RNN, LSTM and GRU are applied. Sequence learning and Attention mechanism have been implemented. Convolutional architectures of CNN, Wavenet, and temporal convolutional network have also been experimented for our problem. For the methodology, we initially select a single time series from the dataset and apply the statistical and deep learning models. This step in the methodology provides us with a strong fundamental understanding of how deep learning models are tuned to obtain the optimal architecture. Then, using the results from a single time series forecasting problem, we shortlist the most optimal deep learning models and their optimal architectures, to solve the problem of time series forecasting. We conclude that recurrent architectures provide the optimal solutions for our analysis (we define optimality through error minimization), and state-of-the-art models such as attention mechanism and sequence learning provide results within acceptable range, but their models are too computationally expensive to learn for multiple epochs and forecasts. We then conclude our analysis by providing important areas to focus on deep learning for time series forecasting in our future work.

Dynamic Activity Predictions Using Graph-based Neural Networks for Time Series Forecasting

Author : Bhuvan Kumar Chennoju
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Dynamic Activity Predictions Using Graph-based Neural Networks for Time Series Forecasting by : Bhuvan Kumar Chennoju

Download or read book Dynamic Activity Predictions Using Graph-based Neural Networks for Time Series Forecasting written by Bhuvan Kumar Chennoju and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is a vital task in numerous fields, and traditional methods, machine learning models, and neural graph networks have been employed to improve prediction accuracy. However, these techniques need to be revised to understand interdependencies and establishing long-term dependencies when dealing with a network of time series, such as predicting energy demand on interconnected grids. To tackle these challenges, this thesis introduces a framework implementing Attention-based Temporal Graph Convolutional Networks (ATGCNs) that enables holistic treatment of a group of time series while learning inter-dependencies and facilitating message passing for enhanced model efficiency. The major contribution of this thesis lies in developing graph embedding algorithms that convert Microbusiness density data into graph data, considering the spatial distance and time series for the proposed ATGCNs model, enabling dynamic activity predictions. The proposed framework is evaluated through experiments using a U.S. Microbusiness density dataset from the GoDaddy Open Survey. The results reveal that ATGCNs outperform traditional time series statistics and machine learning methods in various evaluation metrics, demonstrating comparable forecasting performance to conventional time series forecasting while addressing network scalability and dynamic nature. Additionally, real-time prediction visualizations based on Tableau were developed to showcase the dynamic nature of predictions in the U.S. Microbusiness density domain. In conclusion, this study’s findings highlight the potential advantages of employing graph-based neural networks for time series forecasting, suggesting that incorporating additional data sources could improve prediction accuracy. As future work, transfer learning with ATGCNs will be applied to new domains such as climate prediction or energy demand on interconnected grids. Furthermore, the graph-embedding algorithm and visualization techniques developed in this project will be applied to new domains and datasets across different domains.

Advances in Time Series Forecasting

Author : Cagdas Hakan Aladag
Publisher : Bentham Science Publications
ISBN 13 : 9781681085296
Total Pages : 196 pages
Book Rating : 4.0/5 (852 download)

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Book Synopsis Advances in Time Series Forecasting by : Cagdas Hakan Aladag

Download or read book Advances in Time Series Forecasting written by Cagdas Hakan Aladag and published by Bentham Science Publications. This book was released on 2017-12-06 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes applications of some powerful forecasting approaches with a focus on fuzzy time series methods. Chapters integrate these methods with concepts such as neural networks, high order multivariate systems, deterministic trends, distance measurement and much more. The chapters are contributed by eminent scholars and serve to motivate and accelerate future progress while introducing new branches of time series forecasting. This book is a valuable resource for MSc and PhD students, academic personnel and researchers seeking updated and critically important information on the concepts of advanced time series forecasting and its applications.

A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data

Download A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data PDF Online Free

Author : Vinay Kumar Reddy Chimmula
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data by : Vinay Kumar Reddy Chimmula

Download or read book A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data written by Vinay Kumar Reddy Chimmula and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In regard to human history, it isn't that long since forecasting transitioned from the spiritual and mythical components into the scientific domain. In recent years, forecasting has become an integral part of the mathematical analysis. It has a wide range of significance in various domains and is critical in some life-saving applications. The crucial element in determining the suitable forecasting model is its accuracy. Most of the existing approaches are either based on the statistical or random analysis. One of the limitations of such models is the failure to capture the nonlinearities that are present in the data. Given the inadequacy of classical models in processing hidden non-linear sequences, deep learning models have been showing better results in time series forecasting applications. We addressed this issue by proposing a deep learning-based LSTM model to solve various time series problems. In order to justify our claims, the proposed LSTM models are tested on various datasets including retail, financial and epidemiological data. Forecasting results of different models show that statistical models outperformed deep learning models on small datasets. Meanwhile, deep learning models performed well on large nonstationary data sets. Deep learning-based time series forecasting models are being used in largescale real-world applications over the last few years. After winning the recent M4 competition, the popularity of Deep Learning based models is not only confirmed to academia but also being used for industrial applications. In addition to that, in this novel research, we modeled the current COVID-19 pandemic using deep learning-based time series modeling. This thesis aims at time series modeling and forecasting under different circumstances using statistical and deep learning approaches for various unexplored applications. We addressed the limitations of traditional time series forecasting procedures and proposed various deep learning architectures with multi-layer Recurrent Networks (RNN) and how they may be exploited for time series forecasting problems. The new abilities of neural networks in generating complex mapping functions, feature extraction tools and support for sequential are provided by Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM). Finally, we outlined the underlying factors behind the success of Deep Learning (DL) methods and given some directions for future applications. keywords: Time Series Forecasting, Infectious.

Artificial neural networks in time series forecasting

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (181 download)

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Book Synopsis Artificial neural networks in time series forecasting by :

Download or read book Artificial neural networks in time series forecasting written by and published by . This book was released on 1909 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Esta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na previsão de séries temporais, em particular de séries financeiras, consideradas uma classe especial de séries temporais, caracteristicamente ruídos e sem periodicidade aparente. O trabalho envolve quatro partes principais: um estudo sobre redes neurais artificiais e séries temporais; a modelagem das RNAs para previsão de séries temporais; o desenvolvimento de um ambiente de simulação; e o estudo de caso. No estudo sobre Redes Neurais Artificiais e séries temporais fez-se um levantamento preliminar das aplicações de RNAs na previsão de séries. Constatou-se a predominância do uso do algoritmos de retropropagação do erro para o treinamento das redes, bem como dos modelos estatísticos de regressão, de médias móveis e de alisamento exponencial nas comparações com os resultados da rede. Na modelagem das RNAs de retropropagação do erro considerou-se três fatores determinantes no desempenho da rede: convergência, generalização e escalabilidade. Para o controle destes fatores usou-se mecanismos como; escolha da função de ativação dos neurônios sigmóide ou tangente hiperbólica; escolha da função erro MSE (Mean Square Error) ou MAD (Mean Absolutd Deviation); e escolha dos parâmetros de controle do gradiente descendente e do temapo de treinamento taxa de aprendizado e termo de momento. Por fim, definiu-se a arquitetura da rede em função da técnica utilizada para a identificação de regularidades na série (windowing) e da otimização dos fatores indicadores de desempenho da rede. O ambiente de simulação foi desenvolvido em linguagem C e contém 3.600 linhas de códigos divididas em três módulos principais: interface com o usuário, simulação e funções secundárias. O módulo de interface com o usuário é responsável pela configuração e parametrização da rede, como também pela visualização gráfica dos resultados; módulo de simulação executa as fases de treinamento e testes das RNAs; o módulo de funções secundárias cuida do pré/pós-processamento dos dados, da manipulação de arquivos e dos cálculos dos métodos de avaliação empregados. No estudo de caso, as RNAs foram modeladas para fazer previsões da série do preço do ouro no mercado internacional. Foram feitas previsões univariadas single e multi-step e previsões multivariadas utilizando taxas de câmbio de moedas estrangeiras. Os métodos utilizandos para a avaliação do desempenho da rede foram: coeficiente U de Theil, MSE (Mean Square Error), NRMSE (Normalized Root Mean Square Error), POCID (Percentage Of Change In Direction), scattergram e comparação gráfica. Os resultados obtidos, além de avaliados com os métodos acima, foram comparados com o modelo de Box-Jenkins e comprovaram a superioridade das RNAs no tratamento de dados não-lineares e altamente ruidosos.

Learning Analytics

Author : Johann Ari Larusson
Publisher : Springer
ISBN 13 : 1461433053
Total Pages : 203 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Learning Analytics by : Johann Ari Larusson

Download or read book Learning Analytics written by Johann Ari Larusson and published by Springer. This book was released on 2014-07-04 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.

Time Series Analysis Using Neural Networks

Author : Ritu Vijay
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783659211812
Total Pages : 60 pages
Book Rating : 4.2/5 (118 download)

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Book Synopsis Time Series Analysis Using Neural Networks by : Ritu Vijay

Download or read book Time Series Analysis Using Neural Networks written by Ritu Vijay and published by LAP Lambert Academic Publishing. This book was released on 2012-08 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. The usage of artificial neural networks for time series analysis relies purely on the data that were observed. As Radial Basis networks with one hidden layer is capable of approximating any measurable function. An artificial neural network is powerful enough to represent any form of time series. The capability to generalize allows artificial neural networks to learn even in the case of noisy and/or missing data. Another advantage over linear models is the network's ability to represent nonlinear time series. Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This book presents an application of the artificial neural network with Radial basis function for accurate prediction of tides. This neural network model predicts the time series data of hourly tides directly while using an an efficient learning process.

Time Series Forecasting Using Statistical And Neural Networks Models

Author : Abdoulaye Camara
Publisher :
ISBN 13 : 9783659944741
Total Pages : 120 pages
Book Rating : 4.9/5 (447 download)

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Book Synopsis Time Series Forecasting Using Statistical And Neural Networks Models by : Abdoulaye Camara

Download or read book Time Series Forecasting Using Statistical And Neural Networks Models written by Abdoulaye Camara and published by . This book was released on 2016-10-04 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Time Series Forecasting Using Dynamic Particle Swarm Optimizer Trained Neural Networks

Author : Salihu Aish Abdulkarim
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Time Series Forecasting Using Dynamic Particle Swarm Optimizer Trained Neural Networks by : Salihu Aish Abdulkarim

Download or read book Time Series Forecasting Using Dynamic Particle Swarm Optimizer Trained Neural Networks written by Salihu Aish Abdulkarim and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is a very important research area because of its practical application in many elds. Due to the importance of time series forecasting, much research e ort has gone into the development of forecasting models and in improving prediction accuracies. The interest in using arti cial neural networks (NNs) to model and forecast time series has been growing. The most popular type of NN is arguably the feedforward NN (FNN). FNNs have structures capable of learning static input-output mappings, suitable for prediction of non-linear stationary time series. To model nonstationary time series, recurrent NNs (RNNs) are often used. The recurrent/delayed connections in RNNs give the network dynamic properties to e ectively handle temporal sequences. These recurent/delayed connections, however, increase the number of weights that are required to be optimized during training of the NN. Particle swarm optimization (PSO) is an e cient population based search algorithm based on the social dynamics of group interactions in bird ocks. Several studies have applied PSO to train NNs for time series forecasting, and the results indicated good performance on stationary time series, and poor performance on non-stationary and highly noisy time series. These studies have assumed static environments, making the original PSO, which was designed for static environments, unsuitable for training NNs for forecasting many real-world time series generated by non-stationary processes. In dealing with non-stationary data, modi ed versions of PSOs for optimization in dynamic environments are used. These dynamic PSOs are yet to be applied to train NNs on forecasting problems. The rst part of this thesis formulates training of a FNN forecaster as a dynamic optimization problem, to investigate the application of a dynamic PSO algorithm to train FNNs in forecasting time series in non-stationary environments. For this purpose, a set of experiments were conducted on ten forecasting problems under nine di erent dynamic scenarios. Results obtained are compared to the results of FNNs trained using a standard PSO and resilient backpropagation (RPROP). The results show that the dynamic PSO algorithm outperform the PSO and RPROP algorithms. These ndings highlight the potential of using dynamic PSO in training FNNs for real-world forecasting applications. The second part of the thesis tests the hypothesis that recurrent/delayed connections are not necessary if a dynamic PSO is used as the training algorithm. For this purpose, set of experiments were carried out on the same problems and under the same dynamic scenarios. Each experiment involves training a FNN using a dynamic PSO algorithm, and comparing the result to that obtained from four di erent types of RNNs (i.e. Elman NN, Jordan NN, Multi-Recurrent NN and Time Delay NN), each trained separately using RPROP, standard PSO and the dynamic PSO algorithm. The results show that the FNNs trained with the dynamic PSO signi cantly outperform all the RNNs trained using any of the algorithms considered. These ndings show that recurrent/delayed connections are not necessary in NNs used for time series forecasting (for the time series considered in this study) as long as a dynamic PSO algorithm is used as the training method.

Neural, Novel & Hybrid Algorithms for Time Series Prediction

Author : Timothy Masters
Publisher :
ISBN 13 :
Total Pages : 548 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Neural, Novel & Hybrid Algorithms for Time Series Prediction by : Timothy Masters

Download or read book Neural, Novel & Hybrid Algorithms for Time Series Prediction written by Timothy Masters and published by . This book was released on 1995-10-20 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative guide to predicting the future using neural, novel, and hybrid algorithms Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more. Neural, Novel & Hybrid Algorithms for Time Series Prediction provides information on: * Robust confidence intervals for predictions made with neural, ARIMA, and other models * Wavelets for detecting features that presage important events * Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks * Hybrid ARMA/neural models to improve the accuracy of predictions * Data reduction and orthogonalization using principal components and related operations * Digital filters for preprocessing to enhance useful information and suppress noise * Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models * Effective preprocessing techniques for prediction with neural networks CD-ROM INCLUDES: * PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area * Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler

Time-Series Forecasting

Author : Chris Chatfield
Publisher : CRC Press
ISBN 13 : 1420036203
Total Pages : 281 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Time-Series Forecasting by : Chris Chatfield

Download or read book Time-Series Forecasting written by Chris Chatfield and published by CRC Press. This book was released on 2000-10-25 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space

A Comparative Study of Different Methods of Predicting Time Series

Author : Sutanuka Bhattacharya
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis A Comparative Study of Different Methods of Predicting Time Series by : Sutanuka Bhattacharya

Download or read book A Comparative Study of Different Methods of Predicting Time Series written by Sutanuka Bhattacharya and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis work presents a comparative study of different methods for predicting future values of time series data and implement them to predict the currency exchange rates. The current thesis focuses mainly on two approaches in predicting a time series. One of them is the traditional statistical approach which involves building models based on certain assumptions and then applying them to do the predictions. The models considered in this thesis are multiple regression, exponential smoothing, double exponential smoothing, Box-Jenkins method, and Winter's method. The second approach is using the concept of training neural nets and pattern recognition. This involves in designing a neural network and training it using different learning methods. The learning algorithms used in the current work involves the backpropagation method, recurrent nets learning method, adaptively trained neural nets, and fuzzy learning methods. In addition to these, some methods for forecasting a chaotic time series and fractional differencing are also mentioned in the thesis. In order to compare the performances of different techniques of forecasting the future values of a time series, experiments were conducted using the exchange rates of different currencies with respect to the US dollar. These exchange rates exhibit a lot of randomness in their behaviour and hence it was very challenging to predict their future values. Different prediction zones were selected to conduct the experiments and analysis of the results have been presented towards the end of the thesis.

Neural Networks for Time Series Analysis

Author : K. Du Plessis
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (956 download)

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Book Synopsis Neural Networks for Time Series Analysis by : K. Du Plessis

Download or read book Neural Networks for Time Series Analysis written by K. Du Plessis and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of a time series is a problem well known to statisticians. Neural networks form the basis of an entirely non-linear approach to the analysis of time series. It has been widely used in pattern recognition, classification and prediction. Recently, reviews from a statistical perspective were done by Cheng and Titterington (1994) and Ripley (1993). One of the most important properties of a neural network is its ability to learn. In neural network methodology, the data set is divided in three different sets, namely a training set, a cross-validation set, and a test set. The training set is used for training the network with the various available learning (optimisation) algorithms. Different algorithms will perform best on different problems. The advantages and limitations of different algorithms in respect of all training problems are discussed. In this dissertation the method of neural networks and that of ARlMA. models are discussed. The procedures of identification, estimation and evaluation of both models are investigated. Many of the standard techniques in statistics can be compared with neural network methodology, especially in applications with large data sets. Additional information available on two discs stored at the Africana section, Merensky Library.