Time Series Discrimination by Higher Order Crossings

Time Series Analysis by Higher Order Crossings

Discrimination of time series based on kernel method

Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than ...

Artificial Higher Order Neural Networks in Time Series Prediction

Real world problems are described by nonlinear and chaotic processes, which makes them hard to model and predict. This chapter first compares the neural network (NN) and the artificial higher order neural network (HONN) and then presents commonly known neural network architectures and a number of HONN architectures. The time series prediction problem is formulated as a system identification pro...

Higher order neural networks for financial time series prediction

Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share...

Towards Resolving Fiber Crossings with Higher Order Tensor Inpainting

The use of second-order tensors for the modeling of data from Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) is limited by their inability to represent more than one dominant direction in cases of crossing fiber bundles or partial voluming. Higher-order tensors have been used in High Angular Resolution Diffusion Imaging (HARDI) to overcome these problems, but their larger number of para...