Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data

by   Zhaoxing Gao, et al.

This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using Principal Component Analysis (PCA) and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time series assigned to it and transfers those factors to one and only one node of the second level. Each 2nd-level computer collects the common factors from its subordinates and performs another PCA to select the 2nd-level common factors. This process is repeated until the central server is reached, which collects common factors from its direct subordinates and performs a final PCA to select the global common factors. The noise terms of the 2nd-level approximate factor model are the unique common factors of the 1st-level clusters. We focus on the case of 2 levels in our theoretical derivations, but the idea can easily be generalized to any finite number of hierarchies. We discuss some clustering methods when the group memberships are unknown and introduce a new diffusion index approach to forecasting. We further extend the analysis to unit-root nonstationary time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size T. We use both simulated data and real examples to assess the performance of the proposed method in finite samples, and compare our method with the commonly used ones in the literature concerning the forecastability of extracted factors.


page 3

page 31


Structural-Factor Modeling of High-Dimensional Time Series: Another Look at Approximate Factor Models with Diverging Eigenvalues

This article proposes a new approach to modeling high-dimensional time s...

Modeling High-Dimensional Unit-Root Time Series

In this paper, we propose a new procedure to build a structural-factor m...

Denoising and Multilinear Dimension-Reduction of High-Dimensional Matrix-Variate Time Series via a Factor Model

This paper proposes a new multilinear projection method for dimension-re...

Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors

This paper proposes a novel dynamic forecasting method using a new super...

Order-preserving factor analysis (OPFA)

We present a novel factor analysis method that can be applied to the dis...

A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data

Faced with the growing research towards crude oil price fluctuations inf...

Deterministic parallel analysis

Factor analysis is widely used in many application areas. The first step...

Please sign up or login with your details

Forgot password? Click here to reset