Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms

References

• Aamer A, Eka Yani L, Alan Priyatna I (2020) Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Oper. Supply Chain Management 14(1):1–13.Google Scholar
• Abhishek V, Dogan M, Jacquillat A (2021) Strategic timing and dynamic pricing for online resource allocation. Management Sci. 67(8):4880–4907.Link, Google Scholar
• Aminikhanghahi S, Cook DJ (2017) A survey of methods for time series change point detection. Knowledge Inform. Systems 51(2):339–367.Crossref, Google Scholar
• Aminikhanghahi S, Wang T, Cook DJ (2019) Real-time change point detection with application to smart home time series data. IEEE Trans. Knowledge Data Engrg. 31(5):1010–1023.Crossref, Google Scholar
• Barrios JM, Hochberg YV, Yi H (2022) Launching with a parachute: The gig economy and new business formation. J. Financial Econom. 144(1):22–43.Crossref, Google Scholar
• Bauwens L, Koop G, Korobilis D, Rombouts J (2015) The contribution of structural break models to forecasting macroeconomic series. J. Appl. Econometrics 30(4):596–620.Crossref, Google Scholar
• Besbes O, Castro F, Lobel I (2021) Surge pricing and its spatial supply response. Management Sci. 67(3):1350–1367.Link, Google Scholar
• Bimpikis K, Candogan O, Saban D (2019) Spatial pricing in ride-sharing networks. Oper. Res. 67(3):744–769.Link, Google Scholar
• Boot T, Pick A (2020) Does modeling a structural break improve forecast accuracy? J. Econometrics 215(1):35–59.Crossref, Google Scholar
• Brau R, Aloysius J, Siemsen E (2023) Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics. J. Oper. Management 69(6):965–982.Crossref, Google Scholar
• Cao Y, Wen Z, Kveton B, Xie Y (2019) Nearly optimal adaptive procedure with change detection for piecewise-stationary bandit. Chaudhuri K, Sugiyama M, eds. Proc. 22nd Internat. Conf. Artificial Intelligence Statist., vol. 89 (PMLR, New York), 418–427.Google Scholar
• Chen Y, Wang T, Samworth RJ (2022) High-dimensional, multiscale online changepoint detection. J. Roy. Statist. Soc. B 84(1):234–266.Crossref, Google Scholar
• Chen MK, Chevalier JA, Rossi PE, Oehlsen E (2019) The value of flexible work: Evidence from uber drivers. J. Political Econom. 127(6):2735–2794.Crossref, Google Scholar
• Cheung WC, Simchi-Levi D, Zhu R (2022) Hedging the drift: Learning to optimize under nonstationarity. Management Sci. 68(3):1696–1713.Link, Google Scholar
• Chib S (1998) Estimation and comparison of multiple change-point models. J. Econometrics 86(2):221–241.Crossref, Google Scholar
• Claeskens G, Magnus JR, Vasnev AL, Wang W (2016) The forecast combination puzzle: A simple theoretical explanation. Internat. J. Forecasting 32(3):754–762.Crossref, Google Scholar
• Dufays A, Rombouts JV (2020) Relevant parameter changes in structural break models. J. Econometrics 217(1):46–78.Crossref, Google Scholar
• Elliott G, Timmermann A (2016) Economic Forecasting (Princeton University Press, Princeton, NJ).Google Scholar
• Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
• Fang X, Sheng ORL, Goes P (2013) When is the right time to refresh knowledge discovered from data? Oper. Res. 61(1):32–44.Link, Google Scholar
• Federici M, Tomioka R, Forré P (2022) An information-theoretic approach to distribution shifts. Ranzato M, Beygelzimer A, Dauphin Y, Liang P, Vaughan JW, eds. Adv. Neural Inform. Processing Systems, vol. 34 (Curran Associates, Inc., Red Hook, NY), 17628–17641.Google Scholar
• Friedman J, Tibshirani R, Hastie T (2010) Regularization paths for generalized linear models via coordinate descent. J. Statist. Software 33(1):1–22.Crossref, Google Scholar
• Fryzlewicz P (2014) Wild binary segmentation for multiple change-point detection. Ann. Statist. 42(6):2243–2281.Crossref, Google Scholar
• Garg N, Nazerzadeh H (2022) Driver surge pricing. Management Sci. 68(5):3219–3235.Link, Google Scholar
• Giacomini R, Rossi B (2009) Detecting and predicting forecast breakdowns. Rev. Econom. Stud. 76(2):669–705.Crossref, Google Scholar
• Guda H, Subramanian U (2019) Your Uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives. Management Sci. 65(5):1995–2014.Abstract, Google Scholar
• Hevner A, March S, Park J, Ram S (2004) Design science in information systems research. Management Inform. Systems Quart. 28(1):75–105.Crossref, Google Scholar
• Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: The forecast package for R. J. Statist. Software 26(3):1–22.Google Scholar
• Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2023) forecast: Forecasting functions for time series and linear models. R package version 8.21. Accessed March 24, 2024, https://pkg.robjhyndman.com/forecast/.Google Scholar
• Ketter W, Schroer K, Valogianni K (2022) Information systems research for smart sustainable mobility: A framework and call for action. Inform. Systems Res. 34(3):1045–1065.Link, Google Scholar
• Khosrowabadi N, Hoberg K, Imdahl C (2022) Evaluating human behaviour in response to AI recommendations for judgemental forecasting. Eur. J. Oper. Res. 303(3):1151–1167.Crossref, Google Scholar
• Killick R, Eckley I (2014) changepoint: An R package for changepoint analysis. J. Statist. Software 58(3):1–19.Crossref, Google Scholar
• Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost. J. Amer. Statist. Assoc. 107(500):1590–1598.Crossref, Google Scholar
• Knoblauch J, Jewson JE, Damoulas T (2018) Doubly robust Bayesian inference for non-stationary streaming data with beta-divergences. Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 31 (Curran Associates, Inc., Red Hook, NY).Google Scholar
• Kremer M, Moritz B, Siemsen E (2011) Demand forecasting behavior: System neglect and change detection. Management Sci. 57(10):1827–1843.Link, Google Scholar
• Liu S, He L, Shen MZJ (2021) On-time last-mile delivery: Order assignment with travel-time predictors. Management Sci. 67(7):4095–4119.Link, Google Scholar
• Liu X, Wang GA, Fan W, Zhang Z (2020) Finding useful solutions in online knowledge communities: A theory-driven design and multilevel analysis. Inform. Systems Res. 31(3):731–752.Link, Google Scholar
• Luo L, Song PXK (2020) Renewable estimation and incremental inference in generalized linear models with streaming data sets. J. Roy. Statist. Soc. B 82(1):69–97.Crossref, Google Scholar
• Luo L, Zhou L, Song PXK (2023) Real-time regression analysis of streaming clustered data with possible abnormal data batches. J. Amer. Statist. Assoc. 118(543):2029–2044.Crossref, Google Scholar
• Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J. Econometrics 135(1–2):499–526.Crossref, Google Scholar
• O’Brien L (2023) h3jsr: Access Uber’s H3 Library. R package version 1.3.1. Accessed March 24, 2024, https://obrl-soil.github.io/h3jsr/.Google Scholar
• Parker GG, Van Alstyne MW (2005) Two-sided network effects: A theory of information product design. Management Sci. 51(10):1494–1504.Link, Google Scholar
• Perera HN, Hurley J, Fahimnia B, Reisi M (2019) The human factor in supply chain forecasting: A systematic review. Eur. J. Oper. Res. 274(2):574–600.Crossref, Google Scholar
• Pesaran MH, Timmermann A (2007) Selection of estimation window in the presence of breaks. J. Econometrics 137(1):134–161.Crossref, Google Scholar
• Pesaran MH, Pick A, Pranovich M (2013) Optimal forecasts in the presence of structural breaks. J. Econometrics 177(2):134–152.Crossref, Google Scholar
• Petropoulos F, Apiletti D, Assimakopoulos V, Babai MZ, Barrow DK, Taieb SB, Bergmeir C, et al. (2022) Forecasting: Theory and practice. Internat. J. Forecasting 38(3):705–871.Crossref, Google Scholar
• R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed March 24, 2024, https://www.R-project.org/.Google Scholar
• Rochet JC, Tirole J (2003) Platform competition in two-sided markets. J. Eur. Econom. Assoc. 1(4):990–1029.Crossref, Google Scholar
• Rossi B (2021) Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them. J. Econom. Literature 59(4):1135–1190.Crossref, Google Scholar
• Seyedan M, Mafakheri F (2020) Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. J. Big Data 7(1):1–22.Crossref, Google Scholar
• Taylor SJ, Letham B (2018) Forecasting at scale. Amer. Statist. 72(1):37–45.Crossref, Google Scholar
• Taylor S, Letham B (2021) Prophet: Automatic forecasting procedure. R package version 0.1.0. Accessed March 24, 2024, https://CRAN.R-project.org/package=prophet.Google Scholar
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
• United Nations Conference on Trade and Development (2021) Estimates of global e-commerce 2019 and preliminary assessment of covid-19 impact on online retail 2020. United Nations Conf. Trade Development.Google Scholar
• van den Burg GJJ, Williams CKI (2022) An evaluation of change point detection algorithms. Preprint, submitted March 13, 2020, https://arxiv.org/abs/2003.06222.Google Scholar
• Wang Y, Currim F, Ram S (2022) Deep learning of spatiotemporal patterns for urban mobility prediction using big data. Inform. Systems Res. 33(2):579–598.Link, Google Scholar
• Wu J, Zheng ZE, Zhao JL (2021) Fairplay: Detecting and deterring online customer misbehavior. Inform. Systems Res. 32(4):1323–1346.Link, Google Scholar
• Yang K, Lau RYK, Abbasi A (2023) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.Link, Google Scholar
• Zhang H, Zhao X, Fang X, Chen B (2023) Proactive resource request for disaster response: A deep learning-based optimization model. Inform. Systems Res., ePub ahead of print September 6, https://doi.org/10.1287/isre.2022.0125.Link, Google Scholar
• Zhang M, Marklund H, Dhawan N, Gupta A, Levine S, Finn C (2021) Adaptive risk minimization: Learning to adapt to domain shift. Ranzato M, Beygelzimer A, Dauphin Y, Liang P, Vaughan JW, eds. Adv. Neural Inform. Processing Systems, vol. 34 (Curran Associates, Inc., Red Hook, NY), 23664–23678.Google Scholar