Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices

Author:

Filelis-Papadopoulos Christos K.ORCID,Kyziropoulos Panagiotis E.ORCID,Morrison John P.ORCID,O‘Reilly PhilipORCID

Abstract

AbstractTime series modelling and forecasting techniques have a wide spectrum of applications in several fields including economics, finance, engineering and computer science. Most available modelling and forecasting techniques are applicable to a specific underlying phenomenon and its properties and lack generality of application, while more general forecasting techniques require substantial computational time for training and application. Herewith, we present a general modelling framework based on a recursive Schur - complement technique, that utilizes a set of basis functions, either linear or non-linear, to form a model for a general time series. The basis functions need not be orthogonal and their number is determined adaptively based on fitting accuracy. Moreover, no assumptions are required for the input data. The coefficients for the basis functions are computed using a recursive pseudoinverse matrix, thus they can be recomputed for different input data. The case of sinusoidal basis functions is presented. Discussions around stability of the resulting model and choice of basis functions is also provided. Numerical results depicting the applicability and effectiveness of the proposed technique are given.

Publisher

Springer International Publishing

Reference27 articles.

1. Awartani, B.M., Corradi, V.: Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries. Int. J. Forecast. 21(1), 167–183 (2005)

2. Box, G.E.P., Jenkins, G.M.: Time Series Analysis Forecasting and Control. Holden Day, San Francisco (1976)

3. Brown, R.G.: Smoothing. Forecasting and prediction of discrete time series. Englewood Cliffs, NJ, Prentice Hall (1963)

4. Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on loess (with discussion). J. Official Stat. 6, 3–73 (1990)

5. Dagum, E.B.: Revisions of time varying seasonal filters. J. Forecast. 1(2), 173–187 (1982). https://doi.org/10.1002/for.3980010204

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3