{ "_id": "6a12d0c5acfb0bcc41d22216", "Package": "decompML", "Type": "Package", "Title": "Decomposition Based Machine Learning Model", "Version": "0.1.1", "Authors@R": "c(person(given = \"Girish Kumar\",\nfamily = \"Jha\",\nrole = c(\"aut\", \"ctb\")),\nperson(given = \"Kapil\",\nfamily = \"Choudhary\",\nrole = c(\"aut\", \"cre\"),\nemail = \"kapiliasri@gmail.com\"),\nperson(given = \"Rajender\",\nfamily = \"Parsad\",\nrole = \"ctb\"),\nperson(given = \"Ronit\",\nfamily = \"Jaiswal\",\nrole = \"ctb\"),\nperson(given = \"Rajeev Ranjan\",\nfamily = \"Kumar\",\nrole = \"ctb\"),\nperson(given = \"P\",\nfamily = \"Venkatesh\",\nrole = \"ctb\"),\nperson(given = \"Dwijesh Chandra\",\nfamily = \"Mishra\",\nrole = \"ctb\"))", "Maintainer": "Kapil Choudhary ", "Description": "The hybrid model is a highly effective forecasting\napproach that integrates decomposition techniques with machine\nlearning to enhance time series prediction accuracy. Each\ndecomposition technique breaks down a time series into multiple\nintrinsic mode functions (IMFs), which are then individually\nmodeled and forecasted using machine learning algorithms. The\nfinal forecast is obtained by aggregating the predictions of\nall IMFs, producing an ensemble output for the time series. The\nperformance of the developed models is evaluated using\ninternational monthly maize price data, assessed through\nmetrics such as root mean squared error (RMSE), mean absolute\npercentage error (MAPE), and mean absolute error (MAE). For\nmethod details see Choudhary, K. et al. 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