Time Series Prediction
Rolling Window Time Series Prediction Using MapReduce
... Historically time series prediction was performed by statistician and ...of time series, manual inspection of time series has become time-consuming, cumbersome and ...
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Analysis of Time Series Prediction using Recurrent Neural Networks
... Time series prediction is the heart of forecasting data that is based on past information of any particular dataset, recurrent neural network combines with the time series algorithm and ...
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LTR – MDTS structure – A structure for Multiple Dependent Time Series Prediction
... dependent time series prediction / ...of prediction of synchronized basketball referees’ movement during a basketball ...of time series stem- ming from the aforementioned ...
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A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks
... a time series prediction scheme involving the nonli- near autoregressive algorithm and its ...to time series without noise, while the second one can also be applied for noisy data- ...
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Vehicle's velocity time series prediction using neural network
... velocity time series prediction is presented based on the history of vehicle’s motion using neural ...driving time series are used for the ...data prediction where two separate ...
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Application of Higher Order Neural Networks to Financial Time Series Prediction
... Traditional areas in which ANNs are known to excel are pattern recognition, pattern matching, and mathematical function approximation (nonlinear regression). However, they suffer from several well-known limitations. They ...
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Time Series Prediction using Multiwavelet Transform and Echo State Network
... of time series prediction using neural network techniques was increased ...for time series prediction have been ...the time series explicitly within their internal ...
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Financial Time Series Prediction Using Spiking Neural Networks
... financial time series ...noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude ...favourable prediction results for the Spiking Neural ...
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The FL-SMIA Network: A Novel Architecture for Time Series Prediction
... one-day-ahead prediction tasks for share prices and ex- change rates over the SMIA networks alone and over standard multilayer ...financial time series ...
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A new dynamic approach for non singleton fuzzification in noisy time series prediction
... Fuzzy logic systems (FLSs) have been widely used to tackle time-series prediction problems, particularly under noise and uncertainty conditions. The applied techniques are ranging from type-1 to ...
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Nonstationary time series prediction combined with slow feature analysis
... climate time series have some degree of nonstationarity due to external driving forces perturbing the observed ...a time series from the slow feature anal- ysis (SFA) approach, and then ...
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Combining parametric and nonparametric approaches for more efficient time series prediction
... served to adjust the 3 predictors, and the last m = 100 values were used to compare the actual simulated values and their one-step ahead predictions. Figure 2 compares the distributions of the N m = 5000 ...
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Evolutionary multivariate time series prediction
... Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care ...various prediction models ...
168
Time Series. Prediction
... Totuşi să observăm că acest model implică respectarea unor condiţii de liniaritate şi metodele de determinare a componentelor un sunt satisfăcătoare în toate cazurile. Teoria generală [r] ...
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Enhancement in Financial Time Series Prediction with Feature Extraction in Text Mining Techniques
... The Proposed System deals with a replacement domain- specific topic model, the FinLDA model. It deals with incorporating changes in money statistic into the common Latent Dirichlet Allocation to come up with a ...
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An improved multilayer perceptron based on wavelet approach for physical time series prediction
... for time series data and it is an indispensable task to deal with (Ghosh & Raychaudhuri, ...several time series filters commonly used in research to separate the behavior of the ...
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Gradient radial basis function networks for nonlinear and nonstationary time series prediction
... Simulation results using the classical RBF and GRBF networks to predict the Mackey-Glass chaotic time series with and without timevarying meadtrend are given in Section I11 to demonstrat[r] ...
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Methods for event time series prediction and anomaly detection
... whole time series (including “future” for the outliers) is available, and they look back to find all outliers in the ...real time. Second, most of the above methods assume the time ...
143
Joint Fusion Learning of Multiple Time Series Prediction
... 2) Synthetic Datasets: In order to test our forecasting method, we have designed a synthetic data generator. We develop it in such a way that it can imitate the real world scenarios. To do so, we have implement a very ...
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Hidden Markov Model for Time Series Prediction
... The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. Hidden Markov Model is one of the most ...
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