IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v40y2021i2p228-242.html
   My bibliography  Save this article

A causal model for short‐term time series analysis to predict incoming Medicare workload

Author

Listed:
  • Tasquia Mizan
  • Sharareh Taghipour

Abstract

We have investigated methodologies for predicting radiologists' workload in a short time interval by adopting a machine learning technique. Predicting for shorter intervals requires lower execution time combined with higher accuracy. To deal with this issue, an ensemble model is proposed with the fixed‐batch‐training method. To excel in the execution time, a fixed‐batch‐training method is used. On the other hand, the ensemble of multiple machine learning algorithms provides higher accuracy. The experimental result shows that this predictive model can produce at least 10% higher accuracy in comparison with the other available widely used short‐term time series forecasting models. In the studied medical system, this gain in accuracy for the earlier prediction of workload can reduce the Medicare relative value unit cost by $1.1 million annually, which we have formulated and shown in this paper. The proposed batch‐trained ensemble of experts model has also provided at least a 6% improvement in execution time compared with the other studied models.

Suggested Citation

  • Tasquia Mizan & Sharareh Taghipour, 2021. "A causal model for short‐term time series analysis to predict incoming Medicare workload," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 228-242, March.
  • Handle: RePEc:wly:jforec:v:40:y:2021:i:2:p:228-242
    DOI: 10.1002/for.2717
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2717
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2717?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    3. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    4. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Changqing Cheng & Akkarapol Sa-Ngasoongsong & Omer Beyca & Trung Le & Hui Yang & Zhenyu (James) Kong & Satish T.S. Bukkapatnam, 2015. "Time series forecasting for nonlinear and non-stationary processes: a review and comparative study," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1053-1071, October.
    7. Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
    8. Cheng Ju & Mary Combs & Samuel D. Lendle & Jessica M. Franklin & Richard Wyss & Sebastian Schneeweiss & Mark J. van der Laan, 2019. "Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(12), pages 2216-2236, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Francis Ahking, 2003. "Efficient unit root tests of real exchange rates in the post-Bretton Woods era," Economics Bulletin, AccessEcon, vol. 6(7), pages 1-12.
    3. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    4. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    5. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-37.
    6. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    7. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    8. Cheung, Yin-Wong & Ng, Lilian K., 1998. "International evidence on the stock market and aggregate economic activity," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 281-296, September.
    9. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    10. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
    11. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    12. Peter Uchenna Okoye & Chinwendu Christopher Mbakwe & Evelyn Ndifreke Igbo, 2018. "Modeling the Construction Sector and Oil Prices toward the Growth of the Nigerian Economy: An Econometric Approach," Economies, MDPI, vol. 6(1), pages 1-19, March.
    13. Cribari-Neto, Francisco, 1996. "On time series econometrics," The Quarterly Review of Economics and Finance, Elsevier, vol. 36(Supplemen), pages 37-60.
    14. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    15. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    16. Cheung, Yin-Wong & Chinn, Menzie D, 1997. "Further Investigation of the Uncertain Unit Root in GNP," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 68-73, January.
    17. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
    18. Singh, Nirvikar & Mora, Jesse & Carolan, Terrie, 2012. "Trade Dynamics in the East Asian Miracle: A Time Series Analysis of U.S.-East Asia Commodity Trade, 1962-1992," Santa Cruz Department of Economics, Working Paper Series qt0fm1r83r, Department of Economics, UC Santa Cruz.
    19. Yin Zhang & Guanghua Wan, 2005. "China's Business Cycles: Perspectives from an AD–AS Model," Asian Economic Journal, East Asian Economic Association, vol. 19(4), pages 445-469, December.
    20. Presno, Maria Jose & Lopez, Ana Jesus, 2003. "Response surface estimates of stationarity tests with a structural break," Economics Letters, Elsevier, vol. 78(3), pages 395-399, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:40:y:2021:i:2:p:228-242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.