Fusion ANFIS model based on AR for forecasting EPS of leading industries

Liang Ying Wei, Ching Hsue Cheng, Hsin-Hung Wu

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Earnings per share (EPS) represents the profitability of a common stock and the financial performance of a particular company. Therefore, EPS is often regarded as a major indicator for investors to purchase stocks. The traditional approach is to use a conventional linear time series model for EPS prediction. However, the results would be in doubt when the forecasting problems are nonlinear. For this reason, this paper proposes a fusion forecasting model that incorporates an autoregressive model into an adaptive network-based fuzzy inference system (ANFIS) with three facets: (1) test the lag period of EPS; (2) take fuzzy inference systems (FIS) to fuzzify the past periods of EPS based on the AR concept and use adaptive networks to tune optimal parameters; and (3) employ an integrated ANFIS model to predict EPS. To illustrate the proposed model, 15-quarter EPS data are employed. The experimental results indicate that the proposed model outperforms the listing models.

Original languageEnglish
Pages (from-to)5445-5458
Number of pages14
JournalInternational Journal of Innovative Computing, Information and Control
Issue number9
Publication statusPublished - 2011 Sep 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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