Multiscale Decomposition of Big Data Time Series for Analysis and Prediction of Macroeconomic Data: A Recent Approach | Chapter 04 | Theory and Applications of Mathematical Science Vol. 3

The problem of the extraction of the relevant information for pre- diction purposes in a Big Data time series context is tackled. This issue is especially crucial when the forecasting activity involves macroeconomic time series, i.e. when one is mostly interested in finding leading variables and, at the same time, avoiding overfitted model structures. Unfortunately, the use of big data can cause dangerous overparametrization phenomena in the enter- tained models. In addition, two other drawbacks should be considered: firstly, humandriven handling of big data on a case-by-case basis is an impractical (and generally not viable) option and secondly, focusing solely on the raw time series might lead to suboptimal results. The presented approach deals with these problems using a twofold strategy: i) it expands the data in time scale domain, in the attempt to increase the likelihood of giving emphasis to possibly weak, relevant, signals and ii) carries out a multi-step dimension reduction procedure. The latter task is done by means of crosscorrelation functions (whose employment will be theoretically justified) and a suitable objective function.

Author(s) Details

Livio Fenga
ISTAT, Italian National Institute of Statistics, Italy.

View Books:

Inner Social Interactions Model of Big Data Impact on Economical Framework | Chapter 07 | Current Perspective to Economics and Management Vol. 2

Introduction: An inclusive model of economical-social interactions and its repercussions on Big Data analysis is presented. Many phenomenological topics are involved in this job, such as the idea of complexity, statistical human behavior and market structures. Complexity on social interactions is a polemic subject, and it is also a complicated phenomenon to deal with.

Aims / Objectives: This particular study is aimed to develop some proper mathematical model to justify the big data consuming economical framework with the proper social interactions. So that it can build some major key processes assessing several types of economical frames. 

Study Design: Chain Phenomena Analysis.

Place and Duration of Study: University of Guadalajara, Physics Department, Data Science Group.

Results: Model exposition.

Conclusion: This study shows how, as long as time change currently, social interaction impact on economical framework has become bigger. Big Data tools to manipulate high volume levels of information from these interactions have been a strongest platform to analyse economical indicators, such as those which repercussions affects financial stock markets. This process is modelled in this article. A complete model could be a model that considers as social interactions more factors than just trading. Although we are working to applied this model and test it with concrete data bases and real examples. This set of scalar constants defined with κ might have   empty entries waiting to be filled with experimental data and empirical tuning. Stock Markets one have a better option about predictions then a social interaction based model. We are not saying that this model puts aside regular methods for stock market predictions, but, perhaps this new approach would helps to this purpose.

Author(s) Details

A. Alatorre
Department of Physics, University of Guadalajara, Olimpica Boulevard General Marcelino Garca Barragn 1421, Olmpica, 44430 San Pedro, Tlaquepaque, Jal, Mexico.

Read full article:

View Volume: