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[PDF] Top 20 Using deep learning and evolutionary algorithms for time series forecasting

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Using deep learning and evolutionary algorithms for time series forecasting

Using deep learning and evolutionary algorithms for time series forecasting

... A time series is an ordered sequence of data points, usually measured in uniform time intervals (NIST/SEMATECH, ...of time series is that data observations are interdependent ... See full document

71

An adaptive learning system for time series forecasting in the presence of concept drift

An adaptive learning system for time series forecasting in the presence of concept drift

... ECDD and PHt, in this case, the weighting strategies provided statistically equivalent number of false-alarms and miss-detections than the no weighting ...alarms and miss-detections provided by the ... See full document

151

A Neural Network Approach to Time Series Forecasting

A Neural Network Approach to Time Series Forecasting

... the time series ...ARIMA and GARCH methodology and the MLP algorithm is that they are global approximators, assuming that one relationship fits for all locations in an ...these ... See full document

5

Forecasting small population monthly fertility and mortality data with seasonal time series methods

Forecasting small population monthly fertility and mortality data with seasonal time series methods

... economists and demographers use stochastic time series methods to project the dynamics of fertility, mortality, and ...Monthly time series of live births and deaths ... See full document

32

Agent-based distributed time series forecasting system

Agent-based distributed time series forecasting system

... The current theory of economic forecasting is based almost only on the theory of probability and mathematical statistics. However, practice shows that this is not enough. Nowadays, it is believed that only ... See full document

11

Forecasting the Portuguese stock market time series by using artificial neural networks

Forecasting the Portuguese stock market time series by using artificial neural networks

... data and some typical features are studied, like the Hurst exponents, among ...nonlinearities and the results are as expected: the financial time series do not exhibit linear ...markets ... See full document

14

TSPO: an autoML approach to time series forecasting

TSPO: an autoML approach to time series forecasting

... intervals, and the second is to produce point ...benchmarks and ML ...perceptron and a recurrent neural network with both basic architecture and parametrisation as well as a basic ... See full document

44

Neural networks forecasting and classification-based techniques for novelty detection in time series

Neural networks forecasting and classification-based techniques for novelty detection in time series

... 2.2 neural networks for classification 16 mathematical function [Cyb88]. MLPs with a single hidden layer are the most widely used architecture. However, in some cases the use of two or more hidden layers can easy network ... See full document

201

Gene prediction using Deep Learning

Gene prediction using Deep Learning

... terminator and regulatory regions, classification of homologous sequences and recogni- tion of other specific binding sites within the ...initio and comparative methods to enrich prediction ...our ... See full document

93

Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer

Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer

... model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity ...a forecasting model based on a relatively new Swarm Intelligence ... See full document

6

REPOSITORIO INSTITUCIONAL DA UFOP: Hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.

REPOSITORIO INSTITUCIONAL DA UFOP: Hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.

... load forecasting problem with hourly ...big-data time series, which are reality in several areas (such as in the electric industry, biology, neuroscience, image processing, among others) we decide to ... See full document

6

Forecasting Stock Markets Using Machine Learning

Forecasting Stock Markets Using Machine Learning

... uncertainty and volatility that characterize stock markets makes very hard and sometimes even impossible to predict what will ...can and will happen in financial markets is extremely important ... See full document

58

Biosignals learning and synthesis using deep neural networks

Biosignals learning and synthesis using deep neural networks

... ing and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ...novel ... See full document

17

Ensembles for Time Series Forecasting

Ensembles for Time Series Forecasting

... machine learning to model the learning process of learning algorithms (Brazdil et ...meta- learning methodology to our problem as follows. In the meta-learning step of ADE, the ... See full document

239

Recognition and Tracking of Vehicles in Highways using Deep Learning

Recognition and Tracking of Vehicles in Highways using Deep Learning

... Most deep learning algorithms are based on an optimization algorithm called stochastic gradient ...optimization algorithms for machine learning in general and for deep ... See full document

73

A Novel adaptive learning vector quantization for time series classification

A Novel adaptive learning vector quantization for time series classification

... Time series classification is a problem of interest in several areas of research, containing interesting applications for the use of machine learning ...the algorithms based on Artificial ... See full document

147

Forecasting Subnational Monthly Births and Deaths using Seasonal Time Series Methods

Forecasting Subnational Monthly Births and Deaths using Seasonal Time Series Methods

... Birth and death forecasts can be produced using, among others, statistical time series methods (univariate or multivariate), structural models ...machine learning methods ...annual ... See full document

13

Classification Of Complex UCI Datasets Using Machine Learning And Evolutionary Algorithms

Classification Of Complex UCI Datasets Using Machine Learning And Evolutionary Algorithms

... Accuracy, Time Complexity, MAE and RMSE, MLP, NaiveBayes, RandomForest, J48, Genetic Programming perform comparatively better than others in case of all ...labor and lung cancer NaiveBayes performed ... See full document

10

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

... finance and marketing make use of this method [36, ...a forecasting model, and back propagation is used as a training ...inputs, and one output that is a nonlinear function of the ... See full document

23

Mobile app recommendations using deep learning and big data

Mobile app recommendations using deep learning and big data

... reading and writing capabilities from a long-term memory cell (Goodfellow et ...1937) and von Neumann Architecture (Neumann, ...CPU) and an addressing scheme for an external ...neural and ... See full document

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