Selected article for: "long short term memory and lstm memory"
Author: Zhao, Z.; Nehil-Puleo, K.; Zhao, Y.
Title: How well can we forecast the COVID-19 pandemic with curve fitting and recurrent neural networks?
  • Cord-id: m22qslfa
  • Document date: 2020_5_18
    • ID: m22qslfa
    Snippet: Predictions of the COVID-19 pandemic in USA are compared using curve fitting and various recurrent neural networks (RNNs) including the standard long short-term memory (LSTM) RNN and 10 types of slim LSTM RNNs. The curve fitting method predicts the pandemic would end in early summer but the exact date and scale vary with the evolving data used for fitting. All LSTM RNNs result in short-term (8 to 10 days) predictions with comparable accuracies (smaller than 10 %) to curve fitting---they do not s
    Document: Predictions of the COVID-19 pandemic in USA are compared using curve fitting and various recurrent neural networks (RNNs) including the standard long short-term memory (LSTM) RNN and 10 types of slim LSTM RNNs. The curve fitting method predicts the pandemic would end in early summer but the exact date and scale vary with the evolving data used for fitting. All LSTM RNNs result in short-term (8 to 10 days) predictions with comparable accuracies (smaller than 10 %) to curve fitting---they do not show advantage over curve fitting.

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