Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 41 for “"Time series prediction"”.
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Estimating lower bounds for time series prediction error
Research on how to evaluate the time series prediction algorithms are relatively under investigated compared to those to develop prediction algorithms. This research presents a way to estimate lower bounds for a time series prediction error by utilizing the conditional entropy rate, which allows us …
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Higher order neural networks for financial time series prediction
… to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive …
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Analysis of Machine Learning Algorithms for Time Series Prediction
… in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This …
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Nonlinear modeling of time series prediction in the capital markets
Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 1995.
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An improved multilayer perceptron based on wavelet approach for physical time series prediction
… applied in many problems in the domain of time series prediction. The standard NN adopts computationally intensive training algorithms and can easily get trapped into local minima. To overcome such drawbacks in ordinary NN, this study focuses on using a wavelet technique as a filter at the …
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A modified weight optimisation for higher-order neural network in time series prediction
Most of time series signals are difficult to predict as consist of non-linear, high complexity (noise) and chaotic processes. The challenges in time series prediction are to provide a technique to better understand a dataset. In line with this, the Cuckoo Search (CS) learning algorithm, a kind of …
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An improved Pi-Sigma neural network using error feedback for time series prediction
Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural …
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
… train the MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the …
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DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION
… networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables …
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Prediction of Enrollment using Computational Intelligence
<p>This work presents a study on prediction of university enrollment using three computational intelligence (CI) techniques. The enrollment prediction has been considered as a form of time series prediction using CI techniques that include an artificial neural network (ANN), a neurofuzzy inference …
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Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination
… is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This …
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Time series formalism : a systems approach
Time series data has become a modern day phenomena: from stock market data to social media information, modern day data exists as a continuous flow of information indexed by timestamps. Using this data to gather contextual inference and make future predictions is vital to gaining an analytical …
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Simultaneous estimation approaches to large-scale multivariate regression
… especially in image recognition, gene expression prediction and multivariate time series prediction. Numerous approaches have been developed to solve this problem. Some popular statistical methods are group lasso and multivariate ridge regression. Most existing methods either leverage the …
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Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering
… Special classes of such processes deal with time series of sparse data. Studies in such cases focus in the analysis, construction and prediction in parametric models. Here, we assume several non-linear time series with additive noise components, and the model fitting is proposed in two …
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Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.
Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. …
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Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.
Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. …
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Spatial-Temporal Multivariate Time Series Forecasting Using Graph Neural Networks, with an Application to Traffic Speed Prediction
Accurately forecasting complex, multivariate time series varying across space and time requires models that effectively capture spatial and temporal dependencies inherent in the data. This dissertation introduces a novel architecture based on Graph Neural Networks that addresses the challenges of …
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Intelligent Monitoring of Powerline Vibrations - Sparse Sensing and Predictive Modeling Approach
… conditions and fail to provide real-time insights into the dynamic behavior of conductors. This thesis presents a data-driven framework for real-time monitoring and state estimation of vibration profiles in transmission lines, offering a scalable and intelligent alternative to …
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