Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Big Data Analysis Techniques

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_279-1



Time series forecasting is the process of making predictions of the future values of a time series, i.e., a series of data points ordered by time, based on its past and present data/behavior.


Forecasting is part of the broader area of time series analytics. It is a vital and commonly used tool for planning and decision-making. Forecasting fits a model to a given time series and uses it to infer the future values of this time series. For this task, a lot of methods and approaches have been proposed, ranging from simple statistical models to complex and computation intensive machine learning approaches. The biggest challenge in time series forecasting is to identify the optimal combination of model and parameters for a given time series. The process of conducting time series forecasting can be illustrated using the method proposed by Box et al. ( 2008). Although it was designed for the application of autoregressive (integrated) moving...
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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Databases Systems GroupTechnische Universität DresdenDresdenGermany

Section editors and affiliations

  • Domenico Talia
    • 1
  • Paolo Trunfio
    • 1
  1. 1.DIMESUniversity of CalabriaRendeItaly