time series prediction problem
A local field correlated and Monte Carlo based shallow neural network model for nonlinear time series prediction
... In our work, this shallow neural network is applied to underground water time series prediction problem. The orig- inal underground water data has been obtained for decades [16, 17]. In [15] ...
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Financial-Economic Time Series Modeling and Prediction Techniques – Review
... financial series, such as a stock market index or an exchange rate, using created model is very specific task, which aims at supporting key financial decisions such as selling and ...index prediction is ...
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Application of Differential Evolution Algorithm in Prediction of Time Series Data
... It has been observed that the proposed algorithm has given better output as compare to others. The algorithm has been also tested on different time series data published in different International journals. ...
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The FL-SMIA Network: A Novel Architecture for Time Series Prediction
... the problem of predicting financial time-series data has attracted much interest from both commer- cial and academic communities, which resulted in a wide range of ...Financial time ...
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Finding kernel function for stock market prediction with support vector regression
... on time series or index series in which both of series definitely can be represented as a single in-order sequence (Bao et ...for prediction is, and how to test the fitness of ...
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Outlier Detection in Climatology Time Series with Sliding Window Prediction
... the problem also play an important role in predicting the efficiency of performance of the ...the problem can be extracted and used as an important parameter towards the outlier analysis and detection ...
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A time series classification method for behaviour based dropout prediction
... a time series based approach for dropout prediction and illustrated the potential of the method by applying the methodology to the Open University learning analytics ...several time ...
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Modeling and prediction of time-series of monthly copper prices
... the series diagram and the results of the Dicky- Fuller test, the data was once changed to achieve a stationary time-series; the results of the Dicky- Fuller test confirmed the series to ...
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Preliminary Theory of Set DR of Fuzzy Time Series Forecasting Model
... Time series forecasting method is an ancient topic nearly hundred years of history, but its application universality makes people constantly explore new prediction ...fuzzy time series ...
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Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
... Earthquake prediction may also incorporate time series analysis [4], ...Earthquake prediction, from this point of view, may be classified into a sequence-learning ...
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Learning Financial Time Series for Prediction of the Stock Exchange Market
... the prediction of time series is investigated in [4] with three classical forecasting methods, Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least-Squares Support Vector ...
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Prediction of Oil Demand Based on Time Series Decomposition Method
... with time series decomposition method and AI machine learning method, through the analysis of historical data analysis, forecast a Taiwan treadmill parts manufacturing company orders quantity, to achieve ...
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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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Non-parametric smoothing and prediction for nonlinear circular time series
... the time series itself is somewhat erratic the selected smoothing parameters did not vary much — the ranges of concentration were ...most time points; the sum of errors (8) was ...each time ...
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Transformations and Seasonal Adjustment: Analytic Solutions and Case Studies
... transformation problem has not received sufficient recognition in the current seasonal adjustment ...adjusted series should have the same expectation (average) as the original series over twelve con- ...
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Smart Planning in Solid Waste Management for a Sustainable Smart City
... cities is solid waste management not only in India but for most of the countries in the world. Hence, such a system has to be build which can eradicate this problem or at least reduce it to the minimum level. The ...
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Self organizing map and least square support vector machine method for river flow modelling
... inaccurate prediction will cause such a huge loss and inconvenience to the management and to the ...the problem facing in forecasting ...of prediction for river flow ...
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Vehicle's velocity time series prediction using neural network
... driving time series prediction. The process of driving time series prediction and the results are discussed in the following ...driving time series ...
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Nonstationary time series prediction combined with slow feature analysis
... Many previous studies have concluded that the climate sys- tem is essentially nonstationary (Trenberth, 1990; Tsonis, 1996; Yang and Zhou, 2005; Boucharel et al., 2009). How- ever, the lack of any general theory for ...
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Spatial Prediction of Landslides using Time Series Analysis and Support Vector Machine
... and Selangor area in Malaysia. Dino et al. (2012) have devised a new hybrid approach of integrating K-nearest neighbour and support vector machine, which is the SVM- NN classification. This approach reduces the impact of ...
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