Employing stacked ensemble approach for time series forecasting

被引:2
|
作者
Sharma N. [1 ]
Mangla M. [2 ]
Mohanty S.N. [3 ]
Pattanaik C.R. [4 ]
机构
[1] CSE Department, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab
[2] CSE Department, Lokmanya Tilak College of Engineering, Navi Mumbai
[3] Department of Computer Engineering, College of Engineering Pune, Pune
[4] Department of Computer Science and Engineering, Ajay Binay Institute of Technology, Cuttack, Odisha
关键词
Ensemble modeling; Exponential smoothing; Neural network auto regression; Time series forecasting;
D O I
10.1007/s41870-021-00765-0
中图分类号
学科分类号
摘要
This manuscript presents a novel stack-based multi-level ensemble model to forecast the future incidences of conjunctivitis disease. Besides predicting the frequency of conjunctivitis, the proposed model also enhances accuracy through the use of the ensemble model. A stacked multi-level ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is carried out on the collected dataset and further evaluated for various performance measures. The result shows that the various error metrics of the proposed ensemble is decreased significantly. Considering the RMSE (Root Mean Square Error) error values, for instance, are reduced by 39.23%, 9.11%, 19.48%, and 17.14% in comparison to Auto-ARIMA, NNAR, ETS, and HW model in that order. This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA, NNAR, ETS, and HW model applied discretely. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.
引用
收藏
页码:2075 / 2080
页数:5
相关论文
共 50 条
  • [1] GATE: A guided approach for time series ensemble forecasting
    Sarkar, Md. Rasel
    Anavatti, Sreenatha G.
    Dam, Tanmoy
    Ferdaus, Md. Meftahul
    Tahtali, Murat
    Ramasamy, Savitha
    Pratama, Mahardhika
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 235
  • [2] Ensemble Approach for Time Series Analysis in Demand Forecasting Ensemble Learning
    Akyuz, A. Okay
    Bulbul, Berna Atak
    Uysal, Mitat
    Uysal, M. Ozan
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA), 2017, : 7 - 12
  • [3] Bayesian enhanced ensemble approach (BEEA) for time series forecasting
    Rodriguez Rivero, Cristian
    Pucheta, Julian
    Otano, Paula
    Juarez, Gustavo
    Franco, Leonardo
    Patino, Daniel
    Velazco, Raoul
    [J]. 2018 IEEE BIENNIAL CONGRESS OF ARGENTINA (ARGENCON), 2018,
  • [4] Time Series Forecasting Through a Dynamic Weighted Ensemble Approach
    Adhikari, Ratnadip
    Verma, Ghanshyam
    [J]. PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING, NETWORKING AND INFORMATICS (ICACNI 2015), VOL 1, 2016, 43 : 455 - 465
  • [5] Arbitrated Ensemble for Time Series Forecasting
    Cerqueira, Vitor
    Torgo, Luis
    Pinto, Faboi
    Soares, Carlos
    [J]. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2017, PT II, 2017, 10535 : 478 - 494
  • [6] Ensemble Time Series Forecasting with XCSF
    Sommer, Matthias
    Stein, Anthony
    Haehner, Joerg
    [J]. 2016 IEEE 10TH INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS (SASO), 2016, : 150 - 151
  • [7] A Weight-adjusting Approach on an Ensemble of Classifiers for Time Series Forecasting
    Li, Lin
    Ngan, Chun-Kit
    [J]. PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND DATA MINING (ICISDM 2019), 2019, : 65 - 69
  • [8] A Model Ranking Based Selective Ensemble Approach for Time Series Forecasting
    Adhikari, Ratnadip
    Verma, Ghanshyam
    Khandelwal, Ina
    [J]. INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONVERGENCE (ICCC 2015), 2015, 48 : 14 - 21
  • [9] Forecasting Time Series - A Layered Ensemble Architecture
    Rahman, Md Mustafizur
    Santu, Shubhra Kanti Karmaker
    Islam, Md Monirul
    Murase, Kazuyuki
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 210 - 217
  • [10] AN EXTENSIBLE ENSEMBLE ENVIRONMENT FOR TIME SERIES FORECASTING
    Ribeiro, Claudio
    Goldschmidt, Ronaldo
    Choren, Ricardo
    [J]. ICEIS 2010: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL 2: ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS, 2010, : 404 - 407