Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide
Abstract
This article presents a comprehensive exploration of the adaptation of long short-term memory (LSTM) neural networks for univariate time series forecasting, a critical area in predictive analytics that spans across various industries including finance, healthcare, and energy. Despite the widespread application of LSTM models in multivariate time series prediction, their optimization for univariate datasets – characterized by a single time-dependent variable – presents unique challenges and opportunities. We begin with a foundational overview of LSTM networks, emphasizing their architectural ...
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