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- W4312843640 abstract "Time series forecasting has been a topic of special interest due to its applications in Finance, Physics, Environmental Sciences and many other fields. In this article, we propose two classical-quantum hybrid architectures for time series forecasting with a multilayered structure inspired by the multilayer perceptron (MLP): a Quantum Neural Network (QNN) and a Hybrid-Quantum Neural Network (HQNN). These architectures incorporate quantum variational circuits with specific encoding schemes and the optimization is carried out by a classical computer. The performance of the proposed hybrid models is evaluated in four forecasting problems: Mackey-Glass time series and USD-to-euro currency exchange rate forecasting (univariate time series) as well as Lorenz attractor and prediction of the Box-Jenkins (Gas Furnace) time series (multivariate time series). The experiments were conducted by using the built-in Pennylane simulator lighting.qubit and Pytorch. Finally, these architectures, compared to the MLP, CNN and LSTM show a competitive performance with a similar number of trainable parameters." @default.
- W4312843640 created "2023-01-05" @default.
- W4312843640 creator A5009780735 @default.
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- W4312843640 date "2022-01-01" @default.
- W4312843640 modified "2023-09-25" @default.
- W4312843640 title "Time Series Forecasting with Quantum Machine Learning Architectures" @default.
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- W4312843640 doi "https://doi.org/10.1007/978-3-031-19493-1_6" @default.
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