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  "Package": "hybridts",
  "Title": "Hybrid Time Series Forecasting Using Error Remodeling Approach",
  "Version": "0.1.0",
  "Authors@R": "person(\"Tanujit\", \"Chakraborty\", email = \"tanujitisi@gmail.com\", role = c(\"aut\", \"cre\",\"cph\"))",
  "Author": "Tanujit Chakraborty [aut, cre, cph]",
  "Description": "Method and tool for generating hybrid time series\nforecasts using an error remodeling approach. These forecasting\napproaches utilize a recursive technique for modeling the\nlinearity of the series using a linear method (e.g., ARIMA,\nTheta, etc.) and then models (forecasts) the residuals of the\nlinear forecaster using non-linear neural networks (e.g., ANN,\nARNN, etc.). The hybrid architectures comprise three steps:\nfirstly, the linear patterns of the series are forecasted which\nare followed by an error re-modeling step, and finally, the\nforecasts from both the steps are combined to produce the final\noutput. This method additionally provides the confidence\nintervals as needed. Ten different models can be implemented\nusing this package. This package generates different types of\nhybrid error correction models for time series forecasting\nbased on the algorithms by Zhang. (2003), Chakraborty et al.\n(2019), Chakraborty et al. (2020), Bhattacharyya et al. (2021),\nChakraborty et al. (2022), and Bhattacharyya et al. (2022)\n<doi:10.1016/S0925-2312(01)00702-0>\n<doi:10.1016/j.physa.2019.121266>\n<doi:10.1016/j.chaos.2020.109850>\n<doi:10.1109/IJCNN52387.2021.9533747>\n<doi:10.1007/978-3-030-72834-2_29>\n<doi:10.1007/s11071-021-07099-3>.",
  "Maintainer": "Tanujit Chakraborty <tanujitisi@gmail.com>",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
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    "User": "root"
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-04-11 07:10:08 UTC",
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      "title": "Hybrid ARIMA ANN Forecasting Model",
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      "title": "Hybrid ARIMA ARNN Forecasting Model",
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