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Found 4 papers, 1 papers with code
Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors
The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting.
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data
Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features.
Determination of the effective cointegration rank in high-dimensional time-series predictive regressions
This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression.
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees
In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors due to the computational and under-sampling issues.
