用LSTM网络预测多变量气温，短期效果良好。
Applied Machine Learning Methods with Long-Short Term Memory Based Recurrent Neural Networks for Multivariate Temperature Prediction
- 基于LSTM的深度循环网络建模气温时间序列
- 短期气温预测准确率较高，长期预测效果下降
- 适合气象预测初学者和时序建模实践者
本文介绍如何构建密集且深层的神经网络进行时间序列预测。首先回顾人工智能与机器学习的发展历程与核心概念，随后深入探讨不同神经网络模型在时间序列预测中的应用。本研究使用Python的Jupyter环境，结合TensorFlow与Keras框架实现系统搭建与项目开发。重点展示了一个基于长短期记忆（LSTM）单元的深度循环神经网络在气象数据上的实际应用。结果表明，该方法在短期内能有效预测气温变化；但长期预测仍存在局限性，论文末尾对此进行了讨论。
原文摘要 · Abstract (English)
This paper gives an overview on how to develop a dense and deep neural network for making a time series prediction. First, the history and cornerstones in Artificial Intelligence and Machine Learning will be presented. After a short introduction to the theory of Artificial Intelligence and Machine Learning, the paper will go deeper into the techniques for conducting a time series prediction with different models of neural networks. For this project, Python's development environment Jupyter, extended with the TensorFlow package and deep-learning application Keras is used. The system setup and project framework are explained in more detail before discussing the time series prediction. The main part shows an applied example of time series prediction with weather data. For this work, a deep recurrent neural network with Long Short-Term Memory cells is used to conduct the time series prediction. The results and evaluation of the work show that a weather prediction with deep neural networks can be successful for a short time period. However, there are some drawbacks and limitations with time series prediction, which will be discussed towards the end of the paper.
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