Open AccessApplication and Comparative Assessment of Data Mining and Time Series Forecasting Models to Indian Coal Mining Production and Employment Parameters
Author(s) -
Anupam Anant Kher,
R. R. Yerpude
Publication year - 2021
Publication title -
international journal of next-generation computing
Language(s) - English
Resource type - Journals
eISSN - 2229-4678
pISSN - 0976-5034
DOI - 10.47164/ijngc.v12i5.472
Subject(s) - autoregressive integrated moving average , time series , computer science , data mining , artificial neural network , series (stratigraphy) , technology forecasting , demand forecasting , fuzzy logic , moving average , machine learning , artificial intelligence , operations research , engineering , paleontology , computer vision , biology
Forecasting is designed to help decision making and planning before the actual event occurs. The main purposeof the application of time series forecasting models to Indian mining data is to get insight into the wide-rangingprinciples and methodologies for forecasting various parameters as well as current trends and future perspectives.This paper highlights the application of some major methods of time series forecasting such as the AutoregressiveIntegrated Moving Average (ARIMA) method, Regression method, Fuzzy Time Series method, Group Method ofData Handling (GMDH Model), and Neural Networks. Based on a series of comparative analyses depending uponthe capabilities and limitations of each model, the perspective of the multi-model based forecasting approach ispresented.