Selected article for: "long term short term and lstm model"

Author: Kamdem, Jules Sadefo; Essomba, Rose Bandolo; Njong, James Berinyuy
Title: Deep Learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities
  • Cord-id: buv8z1fs
  • Document date: 2020_8_19
  • ID: buv8z1fs
    Snippet: Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation
    Document: Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.

    Search related documents:
    Co phrase search for related documents
    • absolute percentage and actual predicted: 1, 2, 3
    • absolute percentage and long lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • absolute percentage and long short: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • absolute percentage and long short term: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
    • absolute percentage and long short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
    • absolute percentage and low negative: 1
    • absolute percentage and low remain: 1
    • absolute percentage and lstm arima: 1, 2, 3
    • absolute percentage and lstm model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • absolute percentage and lstm network: 1, 2, 3, 4, 5, 6, 7, 8
    • absolute percentage and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • absolute percentage and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
    • absolute percentage and machine learning model: 1, 2, 3
    • absolute value prediction and machine learning: 1
    • absolute value prediction and machine learning model: 1