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Annals of Data Science
Erschienen in: Annals of Data Science 1/2015

01.03.2015

Forecasting with Big Data: A Review

verfasst von: Hossein Hassani, Emmanuel Sirimal Silva

Erschienen in: Annals of Data Science | Ausgabe 1/2015

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Abstract

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data.
Vorheriger Artikel Preface
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Fußnoten
Literatur
1.
Alessi L, Barigozzi M, Capasso M (2009) Forecasting large datasets with conditionally heteroskedastic dynamic common factors. Working Paper No. 1115, European Central Bank
2.
Altissimo F, Cristadoro R, Forni M, Lippi M, Veronese G (2010) New Eurocoin: tracking economic growth in real time. Rev Econ Stat 92(4):1024–1034CrossRef
3.
Arribas-Bel D (2014) Accidental, open and everywhere: emerging data sources for the understanding of cities. Appl Geogr 49:45–53CrossRef
4.
5.
Bańbura M, Giannone D, Lenza M (2014) Conditional forecasts and scenario analysis with vector autoregressions for large cross-section. Working Papers ECARES ECARES 2014–2015, ULB - Universite Libre de Bruxelles
6.
Bańbura M, Giannone D, Reichlin L (2010) Large bayesian vector autoregressions. J Appl Econom 25(1):71–92CrossRef
7.
Bańbura M, Modugno M (2014) Maximum likelihood sstimation of factor models on datasets with arbitrary pattern of missing data. J Appl Econom 29(1):133–160CrossRef
8.
Banerjee A, Marcellino M, Masten I (2013) Forecasting with factor-augmented error correction models. Int J Forecast (in Press)
9.
Bernanke B, Boivin J, Eliasz PS (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive approach. Quart J Econ 120(1):387–422
10.
11.
Berry M (2000) Data mining techniques and algorithms. Wiley, New York
12.
13.
Bordoloi S, Biswas D, Singh S, Manna UK, Saggar S (2010) Macroeconomic forecasting using dynamic factor models. Reserv Bank India Occas Pap 31(2):69–83
14.
Bounie D (2012) International production and dissemination information: results, methodological issues, and statistical perspectives. Int J Commun 6:1001–1021
15.
Boyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15(5):662–679CrossRef
16.
Brown B, Chui M, Manyika J (2011) Are you ready for the era of ‘big data’? In: Guzzo RA (ed) McKinsey quarterly. McKinsey & Company, New York
17.
Camacho M, Sancho I (2003) Spanish diffusion indexes. Span Econ Rev 5(3):173–203CrossRef
18.
Carriero A, Clark TE, Marcellino M (2012a) Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility. Working Paper, No. 1227, Federal Reserve Bank of Cleveland
19.
Carriero A, Kapetanios G, Marcellino M (2012b) Forecasting government bond yields with large Bayesian vector autoregressions. J Bank Financ 36(7):2026–2047CrossRef
20.
Carriero A, Kapetanios G, Marcellino M (2011) Forecasting large datasets with Bayesian reduced rank multivariate models. J Appl Econom 26(5):735–761CrossRef
21.
Choi H, Varian H (2012) Predicting the resent with google trends. Econ Rec 88(s1):2–9CrossRef
22.
23.
De Mol C, Giannone D, Reichlin L (2008) Forecasting using a large number of predictors: is Bayesian shrinkage a valid alternative to principal components? J Econom 146(2):318–328CrossRef
24.
Diebold FX (2003) ’Big Data’ Dynamic factor models for macroeconomic measurement and forecasting. In: Dewatripont M, Hansen LP, Turnovsky S (eds) Advances in economics and econometrics, eighth world congress of the econometric society. Cambridge University Press, Cambridge, pp 115–122
25.
Doz C, Giannone D, Reichlin L (2012) A quasi-maximum likelihood approach for large, approximate dynamic factor models. Rev Econ Stat 99(4):1014–1024CrossRef
26.
27.
28.
Efron B (2010) Large-scale inference: empirical Bayes methods for sstimation, testing and prediction. Cambridge University Press, CambridgeCrossRef
29.
Einav L, Levin JD (2013) The data revolution and economic analysis. Working Paper No. 19035, National Bureau of Economic Research
30.
Engle RF (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20(3):339–350CrossRef
31.
Engle RF, Kroner K (1995) Multivariate simultaneous GARCH. Econom Theory 11(1):122–150CrossRef
32.
33.
Fischer U, Schildt C, Hartmann C, Lehner W (2013) Forecasting the data cube: A model configuration advisor for multi-dimensional data sets. In: IEEE 29th International conference on data engineering (ICDE), Brisbane, 8–12 April 2013
34.
Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized factor model: identification and estimation. Rev Econ Stat 82(4):540–554CrossRef
35.
Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471):830–840CrossRef
36.
Frutos S, Menasalva E, Montes C, Segovia J (2003) Calculating economic indexes per household and censal section from official Spanish databases. Intellt Data Anal 7(6):603–613
37.
38.
Garber SC (2009) Census mail list trimming using SAS data mining. In: RRD Research Report, Washington, DC, 9 May 2009
39.
Ghodsi M (2014) A brief review of data mining applications in the energy industry. Int J Energy Stat 2(1):49–57
40.
Gilary A (2011) Recursive partitioning for racial classification cells. In: Proceedings of the survey research methods section, American Statistical Association-Session 628: survey analysis and issues with ata quality 2011, Miami Beach, p 2706
41.
Giovanelli A (2012) Nonlinear forecasting using large datasets: evidence on US and Euro area economies. CEIS Tor Vergata, Research paper series, 10(13). doi:10.​2139/​ssrn.​2172399
42.
Gupta R, Kabundi A, Miller S, Uwilingiye J (2013) Using large datasets to forecast sectoral unemployment. Stat Methods Appl 23(2):229–264CrossRef
43.
44.
45.
Han J, Kamber M, Pie J (2012) Data mining: concepts and techniques. Elsevier, Inc., San FranciscoCrossRef
46.
Hand DJ (2009) Mining the past to determine the future: problems and possibilities. Int J Forecast 25(3):441–451CrossRef
47.
Hassani H, Heravi S, Zhigljavsky A (2009) Forecasting European industrial production with singular spectrum analysis. Int J Forecast 25(1):103–118CrossRef
48.
Hassani H, Heravi S, Zhigljavsky A (2013) Forecasting UK industrial production with multivariate singular spectrum analysis. J Forecast 32(5):395–408CrossRef
49.
Hassani H, Saporta G, Silva ES (2014) Data mining and official statistics: the past, the present & the future. Big Data 2(1):BD1–BD10CrossRef
50.
Hassani H, Webster A, Silva ES, Heravi H (2015) Forecasting US tourist arrivals using optimal singular spectrum analysis. Tour Manag 46:322–335
51.
Hyndman RJ, Athanasopoulos G (2013) Forecasting: principles and practice. Otexts, Melbourne
52.
Jadhav DK (2013) Big data: the new challenges in data mining. Int J Innov Res ComputSci & Technol 1(2):39–42
53.
54.
Kapetanios G, Marcellino M (2009) A parametric estimation method for dynamic factor models of large dimensions. J Time Ser Anal 30(2):208–238CrossRef
55.
56.
Koop GM (2013) Forecasting with medium and large Bayesian VARs. J Appl Econom 28(2):177–203CrossRef
57.
Kopoin A, Moran K, Paré JP (2013) Forecasting regional GDP with factor models: how useful are national and international data? Econ Lett 121(2):267–270CrossRef
58.
Kurgan L, Musilek P (2006) A survey of knowledge discover and data mining process models. Knowl Eng Rev 21(1):1–24CrossRef
59.
60.
61.
Lutz RW, Buhlmann P (2006) Boosting for high multivariate responses in high-dimensional linear regression. Stat Sin 16:471–494
62.
Madden S (2012) From databases to big data. IEEE Internet Comput 16(3):4–6CrossRef
63.
64.
Marz N, Warren J (2013) Big Data: principles and best practices of scalable reatime data systems. Manning Publications, St. Louis
65.
McCarthy JS, Jacob T, Atkinson D (2009) Innovative uses of data mining techniques in the production of official statistics. In: UN statistical commission session on innovations in official statistics, New York, 20 Feb 2009
66.
McCarthy J, Jacob T, McCracken A (2010) Modeling NASS survey non-response using classification trees. In: RDD Research Report, Washington DC, 1 Nov 2010
67.
68.
Nordbotten S (1996) Neural network imputation applied to the Norwegian 1990 population census data. J Off Stat 12(4):385–401
69.
Nguyen HT, Nabney IT (2010) Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. Energy 35(9):3674–3685CrossRef
70.
71.
Paaß G, Kindermann J (2003) Bayesian regression mixtures of experts for geo-referenced data. Intell Data Anal 7(6):567–582
72.
73.
74.
Pyle D (2003) Business modeling and data mining. Elsevier Science, Philadelphia
75.
Rey T, Wells C (2013) Integrating data mining and forecasting. OR/MS Today, New York
76.
Richards NM, King JH (2013) Three paradoxes of big data. Stanf Law Rev Online 66(41):41–46
77.
Schumacher C (2007) Forecasting German GDP using alternative factor odels based on large datasets. J Forecast 26(4):271–302CrossRef
78.
Schumacher C, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398CrossRef
79.
Shi Y (2014) Big Data: history, current status, and challenges going forward. Bridge 44(4):6–11
80.
81.
Silva ES (2013) A combination forecast for energy-related \(\text{ CO }_{2}\) emissions in the United States. Int J Energy Stat 1(4):269–279CrossRef
82.
83.
Silva ES, Wu Y, Ojiako U (2013) Developing risk management as a competitive capability. Strateg Change 22(5–6):281–294CrossRef
84.
Silver N (2012) The signal and the noise: the art and science of prediction. Penguin Books, Westmins
85.
Skupin A, Agarwal P (2007) Introduction: what is a self-organizing map? In: Agarwal P, Skupin A (eds) Self-organizing maps: applications in geographic information science. John Wiley, Chichester
86.
Smolan R, Erwitt J (2012) The human face of big data. Sterling Publishing, New York
87.
Stock JH, Watson MW (2002) Forecasting using principal components from a large number of predictors. J Am Stat Assoc 97(460):1167–1179CrossRef
88.
Stock JH, Watson MW (2006) Forecasting with many predictors. In: Elliott G, Granger CWJ, Timmermann A (eds) Handbook of economic forecasting. Elsevier, Amsterdam, pp 517–554
89.
Stock JH, Watson MW (2008) Forecasting in dynamic factor models subject to structural instability. In: Hendry David F, Castle J, Shephard N (eds) The methodology and practice of econometrics: a festschrift in honour of professor. Oxford University Press, Oxford, pp 173–205
90.
91.
92.
Varian HR (2014) Big data: new tricks for econometrics. J Econ Perspect 28(2):3–28CrossRef
93.
Wang X (2013) Electricity consumption forecasting in the age of big data. Telkomnika 11(9):5262–5266
94.
95.
96.
Wu S, Kang N, Yang L (2007) Fraudulent behaviour forecast in telecom industry based on data mining technology. Commun IIMA 7(4):1–6
Metadaten
Titel
Forecasting with Big Data: A Review
verfasst von
Hossein Hassani
Emmanuel Sirimal Silva
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 1/2015
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-015-0029-9

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