Open AccessA Survey on Time Series Forecasting Approaches and Applications
Author(s) -
Apoorva Thakur,
Sandeep Monga
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41879
Subject(s) - vagueness , computer science , time series , series (stratigraphy) , task (project management) , service (business) , operations research , regularization (linguistics) , econometrics , artificial intelligence , machine learning , economics , engineering , fuzzy logic , business , marketing , systems engineering , paleontology , biology
This Time series forecasting (TSF) assists in making better strategic decisions under uncertain circumstances so that financial crisis can be avoided, wise investments can be made, under/over contracting of utility can be avoided, staffs can be scheduled appropriately, service providers can provide better service, mankind can get prepared for natural disasters and many more.However, the accuracy in forecasting plays a vital role and achieving such is a challenging task owing to the vagueness and nonlinearity associated with most of the real world time series. Therefore, improving the forecasting accuracy has become a keen area of interest among the forecasters from different domains of science and engineering. In this work a survey on time series forecasting approaches on various applications has been performed. Keywords: Time series forecasting, Regularization, Regression