作者：禅与计算机程序设计艺术

1.简介

Long Short-Term Memory (LSTM) networks have been widely used in various time series forecasting applications such as weather prediction, stock market analysis and natural language processing. However, they are known to be highly vulnerable to the vanishing gradient problem during training, which can cause them to lose their ability to learn long-term dependencies between consecutive data points. To address this issue, two recent approaches have been proposed - ConvLSTM and CLSTMResNet - that use convolutional neural networks (CNNs) along with LSTMs.

In this paper, we present a new approach called Convolutional Long Short-Term Memory (ConvLSTM) Neural Network for time series forecasting using CNNs and LSTMs. The main idea behind our model is to combine the benefits of both CNNs and LSTMs while maintaining the architecture simplicity and effectiveness of traditional RNNs. We also introduce an improved version of residual

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