Time series data modeling and prediction of liquid petroleum gas

×

Error message

  • User warning: The following theme is missing from the file system: journalijdr. For information about how to fix this, see the documentation page. in _drupal_trigger_error_with_delayed_logging() (line 1138 of /home2/journalijdr/public_html/includes/bootstrap.inc).
  • Deprecated function: implode(): Passing glue string after array is deprecated. Swap the parameters in drupal_get_feeds() (line 394 of /home2/journalijdr/public_html/includes/common.inc).
  • Deprecated function: The each() function is deprecated. This message will be suppressed on further calls in _menu_load_objects() (line 579 of /home2/journalijdr/public_html/includes/menu.inc).

International Journal of Development Research

Time series data modeling and prediction of liquid petroleum gas

Abstract: 

Time series modeling and prediction has ultimate meaning to various practical domains. Thus a lot of research works are going on in this subject during several years. Many significant models have been suggested in various literature for improving the precision and effectiveness of time series modeling and prediction. The goal of this article is to present a brief sketch of popular time series prediction models used in practice. We have described here important classes of time series models. We have also discussed about the basic issues related to time series modeling. Here we have collected historical data of Liquid Petroleum Gas (LPG) Domestic consumption from year 2008 to 2014 of every month’s data. We also discussed about various time series models is maintained by giving the experimental prediction results, implemented on time series datasets. While fit a model to a dataset, special care is taken to select and the most ungenerous one. To evaluate prediction precision models fitted to a time series. We have shown the found prediction diagram, which graphically represents the intimacy between the original and prediction observations.

Download PDF: