# 延伸阅读
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 有关时间序列预测和深度学习方法的更多信息，请参阅： 
+  [Amazon Forecast 文档](https://docs.aws.amazon.com/forecast/) 
+  [Amazon Forecast 公开发布博客](https://aws.amazon.com/blogs/aws/amazon-forecast-now-generally-available/) 
+  [Amazon SageMaker 现已提供 DeepAR 算法（更准确）](https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/) 
+  [Amazon SageMaker DeepAR 现在支持缺失值、分类和时间序列特征以及广义频率](https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-deepar-now-supports-missing-values-categorical-and-time-series-features-and-generalized-frequencies/) 
+  [Amazon Forecast 现在可以使用卷积神经网络（CNN）来训练预测模型，这使得速度提高 2 倍且准确性提高 30%](https://aws.amazon.com/blogs/machine-learning/amazon-forecast-can-now-use-convolutional-neural-networks-cnns-to-train-forecasting-models-up-to-2x-faster-with-up-to-30-higher-accuracy/) 
+  [Amazon Forecast 现在支持准确衡量个别商品](https://aws.amazon.com/blogs/machine-learning/amazon-forecast-now-supports-accuracy-measurements-for-individual-items/) 
+  [利用 Amazon Forecast 衡量预测模型准确性以优化业务目标](https://aws.amazon.com/blogs/machine-learning/measuring-forecast-model-accuracy-to-optimize-your-business-objectives-with-amazon-forecast/) 
+  [Amazon Forecast Weather Index – 自动提供当地天气信息，以提高预测模型的准确性](https://aws.amazon.com/blogs/machine-learning/amazon-forecast-weather-index-automatically-include-local-weather-to-increase-your-forecasting-model-accuracy/) 
+  [关于时间序列预测模型的科学论文](https://github.com/awslabs/gluon-ts/blob/master/REFERENCES.md) 
+  [Amazon Forecast 示例 GitHub 页面](https://github.com/aws-samples/amazon-forecast-samples) 
+  [AWS 架构中心](https://aws.amazon.com/architecture/)