# 深入閱讀
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 如需時間序列預測和深度學習方法的其他資訊，請參閱： 
+  [Amazon Forecast 文件](https://docs.aws.amazon.com/forecast/) 
+  [Amazon Forecast 正式版本部落格](https://aws.amazon.com/blogs/aws/amazon-forecast-now-generally-available/) 
+  [現已在 Amazon SageMaker 中提供：更精確的 DeepAR 演算法](https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/) 
+  [Amazon SageMaker DeepAR 現在支援遺漏值、分類和時間序列特徵，以及通用頻率](https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-deepar-now-supports-missing-values-categorical-and-time-series-features-and-generalized-frequencies/) 
+  [Amazon Forecast 現在可以使用卷積神經網路 (CNN) 來訓練預測模型，速度可加快 2 倍，準確性可提高 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 現在支援個別項目的準確性測量](https://aws.amazon.com/blogs/machine-learning/amazon-forecast-now-supports-accuracy-measurements-for-individual-items/) 
+  [透過 Amazon Forecast 測量預測模型準確性，以最佳化您的業務目標](https://aws.amazon.com/blogs/machine-learning/measuring-forecast-model-accuracy-to-optimize-your-business-objectives-with-amazon-forecast/) 
+  [Amazon Forecast 氣象指數 — 自動納入當地天氣以提高預測模型的準確性](https://aws.amazon.com/blogs/machine-learning/amazon-forecast-weather-index-automatically-include-local-weather-to-increase-your-forecasting-model-accuracy/) 
+  [關於時間序列預測模型的科學論文](https://github.com/awslabs/gluon-ts/blob/master/REFERENCES.md) 
+  [Amazon Forecast 樣本 GitHub 頁面](https://github.com/aws-samples/amazon-forecast-samples) 
+  [AWS 架構中心](https://aws.amazon.com/architecture/)