Open AccessA survey of time series forecasting from stochastic method to soft computing
Author(s) -
Putriaji Hendikawati,
. Subanar,
Abdurakhman Abdurakhman,
Tarno
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1613/1/012019
Subject(s) - computer science , soft computing , process (computing) , autoregressive model , series (stratigraphy) , time series , box–jenkins , autoregressive integrated moving average , autoregressive–moving average model , industrial engineering , machine learning , data mining , operations research , econometrics , mathematics , artificial neural network , engineering , paleontology , biology , operating system
Forecasting is a part of statistical modelling that is widely used in various fields because of its benefits in decision-making. The purpose of forecasting is to predict the future values of certain variables that vary with time using its previous values. Forecasting is related to the formation of models and methods that can be used to produce a good forecast. This research is a survey paper research that used a systematic mapping study and systematic literature review. Generally, time series research uses linear time series models, specifically the autoregressive integrated moving average model that has long been used because it has good forecasting accuracy. The successfulness of the Box–Jenkins methodology is based on the reality that various models can imitate the behaviour of various types of series, usually without requiring many parameters to be estimated in the final choice of the model. However, the assumption of stationarity that must be met makes this method inflexible to use. With the advances in computers, forecasting methods from stochastic models to soft computing continue to develop and extend. Soft computing for forecasting can provide more accurate results than traditional methods. Moreover, soft computing has many advantages in terms of the amount of data that can be analysed and the time- and cost-effectiveness of the process.