A Literature Review on Big Data and Time Series

Authors(2) :-Ajla Kirlic, Aldin Hasovic

For people who need to make important decisions there is a lot of data that they need to handle. Huge amount of data known as big data denotes large datasets that have high velocity and variety that makes them hard to handle using some known techniques and tools. Main idea of handling big data is to provide valuable insights for decision makers to make valuable and precise decisions. Best way how to deal with big data is to use big data analytics which includes using time series methodology. This paper has a goal to go through literature that refers to big data, time series and different big data analytics methods using data mining.

Authors and Affiliations

Ajla Kirlic
Information Technology, American University in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina
Aldin Hasovic
BHANSA-BiH Air Navigation Service Agency, Sarajevo, Bosnia And Herzegovina

Big Data, Data Mining, Forecasting, Time Series

  1. Imdadullah. "Time Series Analysis". Basic Statistics and Data Analysis. itfeature.com Retrieved January 2014
  2. Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB, 2002.
  3. S. Papadimitriou and P. S. Yu. Optimal multi-scale patterns in time series streams. In SIGMOD, , 2006.
  4. E. J. Keogh, S. Chu, D. Hart, and M. J. Pazzani. An online algorithm for segmenting time series. In ICDM, 2001.
  5. M. Mathioudakis, N. Koudas, and P. Marbach. Early online identification of attention gathering items in social media. In WSDM , 2010.
  6. J. Leskovec, L. Backstrom, and J. M. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD , 2009.
  7. Y. Sakurai, Y. Matsubara, C. Faloutsos. Mining and Forecasting of Big Time-series Data
  8. Kubick, W.R.: Big Data, Information and Meaning. In: Clinical Trial Insights, (2012)
  9. Russom, P.: Big Data Analytics. In: TDWI Best Practices Report, (2011)
  10. A. Kolmogorov (1941) “Interpolation and extrapolation von stationaren Zufalligen Folgen,” Bulletin of the Academy of Sciences (Nauk), USSR, Ser. Math
  11. N. Wiener (1949) The extrapolation, interpolation and smoothing of stationary time series with engineering applications, Wiley: New York
  12. S. Makridakis, M. Hibon (1979) “Accuracy of forecasting: an empirical investigation (with discussion),” Journal of the Royal Statistical Society A
  13. P. Newbold (1983) “The competition to end all competitions,” Journal of Forecasting, 2(3),
  14. Muth (1960) “Optimal properties of exponential weighted moving average forecasts,” Journal of the American Statistical Association
  15. M. Nerlove (1967) “Distributed lags and Unobserved Components in economic time series,” Ch.6 in Ten Economic Studies in the Tradition of Irving Fisher, W. Fellner et. al. eds., New York: John Wiley & Sons.
  16. Muth (1961) “Rational expectations and the theory of price movements,” Econometrica,
  17. Nerlove and Grether (1970) “Some properties of “Optimal” seasonal adjustment,” Econometrica
  18. Harvey (1989) Forecasting, structural time series models, and the Kalman filter, Cambridge University Press.
  19. A.C. Harvey, G. Gardner, G. Phillips (1980) “An algorithm for exact maximum likelihood estimation by means of Kalman filtering,” Applied Statistics, 29
  20. M. Nerlove (1967) “Distributed lags and Unobserved Components in economic time series,” Ch.6 in Ten Economic Studies in the Tradition of Irving Fisher, W. Fellner et. al. eds., New York: John Wiley & Sons.
  21. J. Ledolter (1984) “Comments on ‘A unified view of statistical forecasting procedures’ by A.C. Harvey,” Journal of Forecasting
  22. Cebr: Data equity, Unlocking the value of big data. in: SAS Reports(2012)
  23. Economist Intelligence Unit: The Deciding Factor: Big Data & Decision Making. In: Capgemini Reports,(2012)
  24. Rey, T., and Wells, C. (2013) Integrating Data Mining and Forecasting. OR/MS Today, 39(6).
  25. Berry, M. (2000) Data Mining Techniques and Algorithms. John Wiley and Sons. 14 Biau, O., and D’Elia, A. (2009). Euro Area GDP Forecasting using Large Survey Datasets. A random forest approach.
  26. Cukier, K. (2010). Data, data everywhere. The Economist.
  27. Silver, N. (2012). The Signal and the Noise: The Art and Science of Prediction. Penguin Books, Australia.
  28. Camacho, M., and Sancho, I. (2003). Spanish Diffusion Indexes. Spanish Economic Review
  29. Stock, J. H., and Watson, M. W. (2006). Forecasting with many predictors. In Handbook of Economic Forecasting, Elliott, G., Granger, C. W. J., Timmermann, A. (eds). Elsevier: Amsterdam
  30. Alessi, L., Barigozzi, M., and Capasso, M. (2009). Forecasting Large Datasets with Conditionally Heteroskedastic Dynamic Common Factors. Working Paper No. 1115, European Central Bank.
  31. Wu, S., Kang, N., and Yang, L. (2007). Fraudulent Behaviour Forecast in Telecom Industry Based on Data Mining Technology. Communications of the IIM
  32. Gursun, G., Crovella, M., and Matta, I (2011). Describing and Forecasting Video Access Patterns. In: INFOCOM ’11: Proceedings of the 30th IEEE International Conference on Computer Communications, IEEE, 2011
  33. Sigrist, F., Kunsch, H. R., and Stahel, W. A. (2012). SPDE based modeling of large space-time data set
  34. Wang, X. (2013). Electricity Consumption Forecasting in the Age of Big Data. Telkomnika
  35. Nguyen, H. T., and Nabney, I. T. (2010). Short-term Electricity Demand and Gas Price Forecasts using Wavelet Transforms and Adaptive Models
  36. Fischer, U., Schildt, C., Hartmann, C., and 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)

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 383-388
Manuscript Number : CSEIT183132
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ajla Kirlic, Aldin Hasovic, "A Literature Review on Big Data and Time Series", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.383-388, January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT183132

Article Preview