时间序列预测实战(十九)魔改Informer模型进行滚动长期预测（科研版本，结果可视化）

资源文件列表(大概)

文件名

大小

model/.idea/

model/.idea/.gitignore

184B

model/.idea/inspectionProfiles/

model/.idea/inspectionProfiles/profiles_settings.xml

174B

model/.idea/inspectionProfiles/Project_Default.xml

886B

model/.idea/misc.xml

288B

model/.idea/model.iml

488B

model/.idea/modules.xml

269B

model/.idea/workspace.xml

6.63KB

model/__pycache__/

model/checkpoints/

model/data/

model/data/.ipynb_checkpoints/

model/data/.ipynb_checkpoints/data_loader-checkpoint.py

13.52KB

model/data/.ipynb_checkpoints/MSST2trainData-checkpoint.csv

455.11KB

model/data/.ipynb_checkpoints/T1testData-checkpoint.csv

189.15KB

model/data/.ipynb_checkpoints/T1trainData-checkpoint.csv

560.76KB

model/data/__init__.py

model/data/__pycache__/

model/data/__pycache__/__init__.cpython-38.pyc

115B

model/data/__pycache__/__init__.cpython-39.pyc

152B

model/data/__pycache__/data_loader.cpython-38.pyc

8.88KB

model/data/__pycache__/data_loader.cpython-39.pyc

8.82KB

model/data/data_loader.py

13.47KB

model/environment.yml

198B

model/ETTh1.csv

2.47MB

model/ETTh1-Test.csv

38.37KB

model/exp/

model/exp/.ipynb_checkpoints/

model/exp/.ipynb_checkpoints/exp_informer (4)-checkpoint.py

15.96KB

model/exp/.ipynb_checkpoints/exp_informer-checkpoint.py

15.98KB

model/exp/__init__.py

model/exp/__pycache__/

model/exp/__pycache__/__init__.cpython-38.pyc

114B

model/exp/__pycache__/__init__.cpython-39.pyc

151B

model/exp/__pycache__/exp_basic.cpython-38.pyc

1.5KB

model/exp/__pycache__/exp_basic.cpython-39.pyc

1.54KB

model/exp/__pycache__/exp_informer.cpython-38.pyc

8.7KB

model/exp/__pycache__/exp_informer.cpython-39.pyc

10.01KB

model/exp/exp_basic.py

875B

model/exp/exp_informer.py

15.3KB

model/layers/

model/layers/__init__.py

model/layers/__pycache__/

model/layers/__pycache__/__init__.cpython-39.pyc

154B

model/layers/__pycache__/AutoCorrelation.cpython-39.pyc

5.36KB

model/layers/__pycache__/Autoformer_EncDec.cpython-39.pyc

6.85KB

model/layers/__pycache__/Conv_Blocks.cpython-39.pyc

2.41KB

model/layers/__pycache__/Crossformer_EncDec.cpython-39.pyc

4.38KB

model/layers/__pycache__/Embed.cpython-39.pyc

7.26KB

model/layers/__pycache__/Embedding.cpython-39.pyc

6.5KB

model/layers/__pycache__/ETSformer_EncDec.cpython-39.pyc

11.86KB

model/layers/__pycache__/FourierCorrelation.cpython-39.pyc

4.89KB

model/layers/__pycache__/Invertible.cpython-39.pyc

3.68KB

model/layers/__pycache__/MultiWaveletCorrelation.cpython-39.pyc

18.06KB

model/layers/__pycache__/Projection.cpython-39.pyc

1.19KB

model/layers/__pycache__/Pyraformer_EncDec.cpython-39.pyc

6.66KB

model/layers/__pycache__/SelfAttention_Family.cpython-39.pyc

8.88KB

model/layers/__pycache__/Transformer_EncDec.cpython-39.pyc

4.48KB

model/layers/__pycache__/TransformerBlocks.cpython-39.pyc

5.26KB

model/layers/AutoCorrelation.py

6.29KB

model/layers/Autoformer_EncDec.py

6.67KB

model/layers/Conv_Blocks.py

2.31KB

model/layers/Crossformer_EncDec.py

4.23KB

model/layers/Embed.py

6.85KB

model/layers/Embedding.py

4.83KB

model/layers/ETSformer_EncDec.py

11.13KB

model/layers/FourierCorrelation.py

7.17KB

model/layers/Invertible.py

3.22KB

model/layers/MultiWaveletCorrelation.py

22.5KB

model/layers/Projection.py

745B

model/layers/Pyraformer_EncDec.py

7.26KB

model/layers/SelfAttention_Family.py

11.78KB

model/layers/Transformer_EncDec.py

4.81KB

model/layers/TransformerBlocks.py

5.2KB

model/main_informer.py

7.62KB

model/models/

model/models/__init__.py

model/models/__pycache__/

model/models/__pycache__/__init__.cpython-39.pyc

154B

model/models/__pycache__/attn.cpython-38.pyc

5KB

model/models/__pycache__/attn.cpython-39.pyc

5.02KB

model/models/__pycache__/Autoformer.cpython-39.pyc

4.3KB

model/models/__pycache__/Crossformer.cpython-39.pyc

4.46KB

model/models/__pycache__/decoder.cpython-38.pyc

1.93KB

model/models/__pycache__/decoder.cpython-39.pyc

1.95KB

model/models/__pycache__/DLinear.cpython-39.pyc

3.21KB

model/models/__pycache__/embed.cpython-38.pyc

5.02KB

model/models/__pycache__/embed.cpython-39.pyc

5.04KB

model/models/__pycache__/encoder.cpython-38.pyc

3.46KB

model/models/__pycache__/encoder.cpython-39.pyc

3.49KB

model/models/__pycache__/ETSformer.cpython-39.pyc

3.63KB

model/models/__pycache__/FEDformer.cpython-39.pyc

5.04KB

model/models/__pycache__/FiLM.cpython-39.pyc

8.95KB

model/models/__pycache__/Informer.cpython-39.pyc

5.67KB

model/models/__pycache__/iTransformer.cpython-39.pyc

3.96KB

model/models/__pycache__/LightTS.cpython-39.pyc

4.23KB

model/models/__pycache__/MICN.cpython-39.pyc

7KB

model/models/__pycache__/model.cpython-38.pyc

4.81KB

model/models/__pycache__/model.cpython-39.pyc

4.65KB

model/models/__pycache__/Nonstationary_Transformer.cpython-39.pyc

6.11KB

model/models/__pycache__/PatchTST.cpython-39.pyc

5.21KB

model/models/__pycache__/Pyraformer.cpython-39.pyc

3.17KB

model/models/__pycache__/Reformer.cpython-39.pyc

3.75KB

model/models/__pycache__/TiDE.cpython-39.pyc

5.44KB

model/models/__pycache__/TimesNet.cpython-39.pyc

5.65KB

model/models/__pycache__/Transformer.cpython-39.pyc

3.75KB

model/models/attn.py

6.03KB

model/models/Autoformer.py

6.7KB

model/models/Crossformer.py

6.21KB

model/models/decoder.py

1.73KB

model/models/DLinear.py

4.48KB

model/models/embed.py

4.04KB

model/models/encoder.py

3.47KB

model/models/ETSformer.py

4.49KB

model/models/FEDformer.py

8.25KB

model/models/FiLM.py

11.39KB

model/models/Informer.py

7.27KB

model/models/iTransformer.py

5.67KB

model/models/LightTS.py

5.21KB

model/models/MICN.py

9.67KB

model/models/model.py

6.97KB

model/models/Nonstationary_Transformer.py

9.57KB

model/models/PatchTST.py

8.54KB

model/models/Pyraformer.py

4.12KB

model/models/Reformer.py

4.97KB

model/models/TiDE.py

6.86KB

model/models/TimesNet.py

8.48KB

model/models/Transformer.py

5.5KB

model/myplot.png

59.13KB

model/utils/

model/utils/__init__.py

model/utils/__pycache__/

model/utils/__pycache__/__init__.cpython-38.pyc

116B

model/utils/__pycache__/__init__.cpython-39.pyc

153B

model/utils/__pycache__/masking.cpython-38.pyc

1.38KB

model/utils/__pycache__/masking.cpython-39.pyc

1.43KB

model/utils/__pycache__/metrics.cpython-38.pyc

1.38KB

model/utils/__pycache__/metrics.cpython-39.pyc

1.42KB

model/utils/__pycache__/timefeatures.cpython-38.pyc

7.11KB

model/utils/__pycache__/timefeatures.cpython-39.pyc

7.16KB

model/utils/__pycache__/tools.cpython-38.pyc

3.18KB

model/utils/__pycache__/tools.cpython-39.pyc

3.21KB

model/utils/masking.py

851B

model/utils/metrics.py

826B

model/utils/timefeatures.py

5.43KB

model/utils/tools.py

2.76KB

资源内容介绍

在之前的文章中我们已经讲过Informer模型了，但是呢官方的预测功能开发的很简陋只能设定固定长度去预测未来固定范围的值，当我们想要发表论文的时候往往这个预测功能是并不能满足的，所以我在官方代码的基础上增添了一个滚动长期预测的功能，这个功能就是指我们可以第一次预测未来24个时间段的值然后我们像模型中填补 24个值再次去预测未来24个时间段的值(填补功能我设置成自动的了无需大家手动填补)，这个功能可以说是很实用的，这样我们可以准确的评估固定时间段的值，当我们实际使用时可以设置自动爬取数据从而产生实际效用。本文修改内容完全为本人个人开发，创作不易所以如果能够帮助到大家希望大家给我的文章点点赞，同时可以关注本专栏(免费阅读)，本专栏持续复现各种的顶会内容，无论你想发顶会还是其它水平的论文都能够对你有所帮助。时间序列预测在许多领域都是关键要素，在这些场景中，我们可以利用大量的时间序列历史数据来进行长期预测，即长序列时间序列预测（LSTF）。然而，现有方法大多设计用于短期问题，如预测48点或更少的数据。随着序列长度的增加，模型的预测能力受到挑战。例如，当预测长度超过48点时，LSTM网络的预测

时间序列预测实战(十九)魔改Informer模型进行滚动长期预测（科研版本，结果可视化）

资源文件列表(大概)

资源内容介绍

用户评论 (0)

相关资源

细胞计数（课程设计项目）.zip

传统rrt算法仿真代码

目标检测目标检测目标检测目标检测目标检测目标检测

不同版本上板测试报错及优化报错

基于 C++ 实现爬山法，模拟退火算法，遗传算法求解N皇后问题

Mini-Omni：语言模型可以在流式传输中聆听、交谈和思考

matlab仿真模型包括双馈和直驱 1.5MW的

yolov5/yolov8中文字体文件

yolov5/yolov8英文字体文件

无标题代码代码代码代码代码

deep-sort-pytorch

onnx2ncnnGUI

时间序列预测实战(十九)魔改Informer模型进行滚动长期预测（科研版本，结果可视化）

资源文件列表(大概)

资源内容介绍

用户评论 (0)

相关资源

细胞计数（课程设计项目）.zip

传统rrt算法仿真代码

目标检测目标检测目标检测目标检测目标检测目标检测

不同版本上板测试报错及优化报错

基于 C++ 实现爬山法，模拟退火算法，遗传算法 求解N皇后问题

Mini-Omni：语言模型可以在流式传输中聆听、交谈和思考

matlab仿真模型 包括双馈和直驱 1.5MW的

yolov5/yolov8中文字体文件

yolov5/yolov8英文字体文件

无标题代码代码代码代码代码

deep-sort-pytorch

onnx2ncnnGUI

基于 C++ 实现爬山法，模拟退火算法，遗传算法求解N皇后问题

matlab仿真模型包括双馈和直驱 1.5MW的