Long sequence time-series forecasting (LSTF) requires a higher prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to accommodate the capacity requirements.
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The LSTF is a critical component in assisting individuals to better plan for the future by forecasting outcomes further in advance. Nevertheless, prior ...
Dec 14, 2020 · Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range ...
Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency ...
Long sequence time-series forecasting (LSTF) has become increas- ingly popular for its wide range of applications.
Nov 12, 2022 · The solution to the Long Sequence Forecasting problem is to capture independencies among the long sequence input and output whereas for the Long ...
Aug 17, 2023 · A lightweight single-hidden layer feedforward neural network (SLFN) combining convolution mapping and time-frequency decomposition called CTFNet is proposed.
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Sep 12, 2024 · Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long ...
Dec 23, 2022 · Forecasting long sequence time series plays a crucial role in many applications such as anomaly detection and financial predictions.
Dec 23, 2023 · Time-series forecasting is used to predict future values based on the observations of historical data, which has been extensively studied in ...
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