best_model_path = trainer.checkpoint_callback.best_model_path best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path) # 计算验证集的平均绝对误差 actuals = torch.cat([y for x, y in iter(val_dataloader
best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)# 计算验证集的平均绝对误差 actuals = torch.cat([yforx, yiniter(val_dataloader)] ) predictions = best_tft.predict(val_dataloader)MAE(predictions, actuals) 看一下表现最差的...
PyTorch-Forecasting一个新的时间序列预测库 时间序列预测在金融、天气预报、销售预测和需求预测等各个领域发挥着至关重要的作用。PyTorch- forecasting是一个建立在PyTorch之上的开源Python包,专门用于简化和增强时间序列的工作。在本文中我们介绍PyTorch-Forecasting的特性和功能,并进行示例代码演示。 PyTorch-Forecasting的安...
它首先使用“best_tft.predict”函数对“new_prediction_data”进行预测,并返回原始预测值和新的x值。然后,它使用“best_tft.plot_prediction”函数绘制前10个预测结果的图形,但不显示未来观察值。 new_raw_predictions 和 new_x 是 best_tft.predict 函数的输出。根据代码,best_tft.predict 函数接受 new_predictio...
Traceback (most recent call last): File ".../test_tft.py", line 155, in <module> trainer.fit( File ".../lightning/pytorch/trainer/trainer.py", line 538, in fit call._call_and_handle_interrupt( File ".../lightning/pytorch/trainer/call.py", line 47, in _call_and_handle_interrupt...
This PR fixes incorrect setting of the python_dependencies tag on PytorchForecastingTFT and PytorchForecastingNBeats in branch main (was using the import name, not the pypi package name)
tft = TemporalFusionTransformer.from_dataset( training, learning_rate=0.03, hidden_size=16, # number of attention heads. Set to 4 for large datasets attention_head_size=1, dropout=0.1, hidden_continuous_size=8, # set to <= hidden_size ...
raw_predictions, x = best_tft.predict(val_dataloader, mode="raw, return_x=True) # show only two examples for demonstration purposes for idx in range(2): best_tft.plot_prediction( x, raw_predictions, idx=indices[idx], add_loss_to_title=SMAPE() ) ...
tft = TemporalFusionTransformer.from_dataset( training, learning_rate=0.03, hidden_size=16, # biggest influence network size attention_head_size=1, dropout=0.2, hidden_continuous_size=8, output_size=7, # by default QuantileLoss has 7 quantiles ...
19Branches25Tags Code This branch is986 commits behindsktime/pytorch-forecasting:main. README MIT license Documentation|Tutorials|Release Notes PyTorch Forecastingis a PyTorch-based package for forecasting time series with state-of-the-art network architectures. It provides a high-level API for training...