Based on the matrix decompositions approach, we propose a new means of creating optimal RBF neural architectures for time series prediction applica- tions ...

At the same time, the optimum lag structure is determined using orthogonal techniques such as QR factorization and singular value decomposition (SVD). We claim ...

Orthogonal projection is a cornerstone of vector space methods, with many diverse applications. These include, but are not limited to,

This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. `Optimal' implies that the.

Statistical forecasting methods assume that the time-series can be made stationary by applying mathematical transformations. A stationaryzed time-series is ...

This paper tries to approach the issues by integrating a learnable, orthonormal transformation into CNNM, with the purpose for converting the series of involute ...

The factor analysis (empirical orthogonal transformation) and descriptive statistical analysis was also carried out for the study areas under investigation. The ...

May 10, 2023 · Singular Spectrum Analysis (SSA) is a non-parametric technique used to analyse and forecast time series data.

Apr 2, 2024 · orthogonal transformation, akin to PCA's decomposition into orthogonal principal components. Thus, applying attention directly to a raw time ...

Feb 1, 2023 · We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning.