Complexity

Complexity in Forecasting and Predictive Models


Publishing date
01 Apr 2019
Status
Published
Submission deadline
30 Nov 2018

1Universidad Pablo de Olavide, Seville, Spain

2Universidade do Algarve, Faro, Portugal

3Universidad Internacional de La Rioja, Logroño, Spain


Complexity in Forecasting and Predictive Models

Description

Predictive modeling is a process of creating a model to predict the future behaviour to support decision-making by diagnosing the real-world applications. This emergence of more complex modeling requirements goes hand-in-hand with and underlines the critical importance of advanced analytical methods, like Neural Networks, Evolutionary Algorithms, Chaotic Models, Cellular Automata, Agent-Based Models, Finite Mixture Partial Least Squares (FIMIX-PLS), and so on.

The distinction between naive, complex, and chaotic systems provides an excellent context for advanced forecasting models. In order to develop forecasting and predictive models, we need to better understand the new trends in computational and statistical techniques which could enable making better forecasts. The main complexity is dealing with the increasing variety and changing data streams, which is forcing scholars to adopt innovative and hybrid methods. Today, new technologies and new sources of data are emerging: IoT, Big Data, Neuromarketing, and so forth.

The purpose of this issue is to trigger a substantive discussion on how forecasting models can face the upcoming complexity challenges. We are interested in original and constructive contributions addressing the recent advances of forecasting in wide sense. Large data volumes are daily generated from heterogeneous sources (e.g., e-Health, social networks, marketing, and financial) through new technologies like Cloud Computing, Distributed Artificial Intelligence, Digital Marketing, Internet of Things, and Neuromarketing, among others.

We are interested in original contributions addressing the recent advances of methodological and practical topics related to the complexity in forecasting and predictive models.

Potential topics include but are not limited to the following:

  • Innovative methods for forecasting
  • Machine learning for big data
  • Block chain and cryptocoins forecasting
  • Chaos-based models for forecasting
  • Search and optimization for Big Data
  • Genetic algorithms for forecasting
  • Advanced predictive models using Big Data analytics
  • Predictive models using FIMIX-PLS
  • Methods for sentiment analysis on Big Data
  • Cellular automata for forecasting
  • Evolutionary game theory applications in forecasting
  • Social network analysis using big data
  • Real-world applications of forecasting and predictive models, like anomaly detection, e-commerce, e-health, and so on

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 8160659
  • - Editorial

Complexity in Forecasting and Predictive Models

Jose L. Salmeron | Marisol B. Correia | Pedro R. Palos-Sanchez
  • Special Issue
  • - Volume 2019
  • - Article ID 5353296
  • - Research Article

An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors

Huixin Tian | Minwei Shuai | ... | Xiao Peng
  • Special Issue
  • - Volume 2019
  • - Article ID 8523748
  • - Research Article

Looking for Accurate Forecasting of Copper TC/RC Benchmark Levels

Francisco J. Díaz-Borrego | María del Mar Miras-Rodríguez | Bernabé Escobar-Pérez
  • Special Issue
  • - Volume 2019
  • - Article ID 6352657
  • - Research Article

The Bass Diffusion Model on Finite Barabasi-Albert Networks

M. L. Bertotti | G. Modanese
  • Special Issue
  • - Volume 2019
  • - Article ID 5039097
  • - Research Article

Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network

Fang Liu | Hua Gong | ... | Ke Xu
  • Special Issue
  • - Volume 2019
  • - Article ID 4164853
  • - Research Article

Green Start-Ups’ Attitudes towards Nature When Complying with the Corporate Law

Rafael Robina-Ramírez | Antonio Fernández-Portillo | Juan Carlos Díaz-Casero
  • Special Issue
  • - Volume 2019
  • - Article ID 4392785
  • - Research Article

A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices

Yingrui Zhou | Taiyong Li | ... | Zijie Qian
  • Special Issue
  • - Volume 2019
  • - Article ID 2782715
  • - Research Article

Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Hafiza Mamona Nazir | Ijaz Hussain | ... | Ishfaq Ahmad
  • Special Issue
  • - Volume 2019
  • - Article ID 7408725
  • - Research Article

End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Chuang Gao | Minggang Shen | ... | Maoxiang Chu
Complexity
 Journal metrics
Acceptance rate38%
Submission to final decision68 days
Acceptance to publication52 days
CiteScore2.690
Impact Factor2.591
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