<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Time Series Forecasting using ARIMA-ANN Hybrid Model</dc:title>
  <dc:title>R package ARIMAANN version 0.1.0</dc:title>
  <dc:description>Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) &lt;doi:10.1016/S0925-2312(01)00702-0&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.3.1), stats,forecast, tseries</dc:relation>
  <dc:creator>Mrinmoy Ray &lt;mrinmoy4848@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Ramasubramanian V. [aut, ctb],
  Mrinmoy Ray [aut, cre]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2022-10-13</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=ARIMAANN</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.ARIMAANN</dc:identifier>
</oai_dc:dc>
