Time series forecasting for uni- variant data using hybrid GA-OLSTM model and performance evaluations

Autor:	Nisha, Thakur, Sanjeev, Karmakar, Sunita, Soni
Rok vydání:	2022
Předmět:	Computational Theory and Mathematics Artificial Intelligence Computer Networks and Communications Applied Mathematics Electrical and Electronic Engineering Computer Science Applications Information Systems
Zdroj:	International Journal of Information Technology. 14:1961-1966
ISSN:	2511-2112 2511-2104
DOI:	10.1007/s41870-022-00914-z
Popis:	Time series forecasting of uni-variant rainfall data is done using a hybrid genetic algorithm integrated with optimized long-short term memory (GA-OLSTM) model. The parameters included for the valuation of the efficiency of the considered model, were mean square error (MSE), root mean square error (RMSE), cosine similarity (CS) and correlation coefficient (
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afa9b264ef6c5e737d430e6203e56691 https://doi.org/10.1007/s41870-022-00914-z Zobrazit plný text záznamu Full text from SpringerLink