{ "_id": "6ae37f3c8d250e7ad09f0aed", "Package": "RWInfoLSTM", "Type": "Package", "Title": "Random Walk-Informed LSTM for Time Series Forecasting", "Version": "0.1.0", "Authors@R": "c(\nperson(given = \"Md\", family = \"Yeasin\", role = \"aut\"),\nperson(given = \"Ranjit Kumar\", family = \"Paul\",\nemail = \"ranjitstat@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(given = \"Pushkar\", family = \"Bora\", role = \"aut\")\n)", "Maintainer": "Ranjit Kumar Paul ", "Description": "Implements a Random Walk (RW)-informed Long Short-Term\nMemory (LSTM) network for univariate time series forecasting.\nThe LSTM is trained on the full series with a composite loss\nthat blends the usual mean squared error (MSE) between\npredictions and observations with the MSE between predictions\nand the fitted Random Walk, weighted by a parameter lambda (0\ngives a standard LSTM, 1 gives a fully RW-guided LSTM). The\nRandom Walk, either simple or with drift, is selected\nautomatically by its in-sample root mean squared error (RMSE),\nand the lambda value with the lowest training RMSE is retained.\nThe LSTM hyperparameters can be supplied manually or tuned by\ngrid search, and functions for out-of-sample forecasting,\naccuracy measures and plots are provided. 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