Open AccessAutoregressive Integrated Moving Average Model for Polar Seas Ice Prediction
Author(s) -
Şafak Kayıkçı
Publication year - 2020
Publication title -
international journal of mathematical models and methods in applied sciences
Language(s) - English
Resource type - Journals
ISSN - 1998-0140
DOI - 10.46300/9101.2020.14.19
Subject(s) - autoregressive integrated moving average , sea ice , climatology , snow , autoregressive model , polar , meteorology , arctic ice pack , environmental science , arctic , data set , computer science , econometrics , time series , geography , geology , oceanography , statistics , mathematics , artificial intelligence , physics , astronomy
Sea ice predictions are very important for the future of polar climates and play a significant role in ecosystems. Models are the simulated representations that have been set up to research systems. To advance model forecasts, researchers require improved parameterizations that are formed by the assembling and analysis of convenient observations. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is proposed to predict the Arctic and Antarctic sea ice extent. The data is gathered from the National Snow and Ice Center (NSIDC) between 01. Jan.1979 and 30. Jun.2020. The fitted data between 2017 and 2020 matches the observed data very closely with the overlap is firmly within the 95% confidence band shows the success of the model.