In the real world, long sequence time-series forecasting (LSTF) is needed in
many cases, such as power consumption prediction and air quality
prediction.Multi-dimensional long time series model has more strict
requirements on the model, which not only needs to effectively capture the
accurate long-term dependence between input and output, but also needs to
capture the relationship between data of different dimensions.Recent research
shows that the Informer model based on Transformer has achieved excellent
performance in long time series prediction.However, this model still has some
deficiencies in multidimensional prediction,it cannot capture the relationship
between different dimensions well. We improved Informer to address its
shortcomings in multidimensional forecasting. First,we introduce an adaptive
graph neural network to capture hidden dimension dependencies in mostly time
series prediction. Secondly,we integrate adaptive graph convolutional networks
into various spatio-temporal series prediction models to solve the defect that
they cannot capture the relationship between different dimensions.
Thirdly,After experimental testing with multiple data sets, the accuracy of our
framework improved by about 10\% after being introduced into the model.

通过将自适应图卷积网络集成到各种空间时序预测模型中，以解决多维预测中维度之间的关系难以捕捉的问题，并进行了多个数据集的实验测试，证明我们的建议框架在模型中引入后，其准确率提高了约 10%。

多维时间序列预测的自适应图卷积网络框架

Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction

Most of the existing tracking methods link the detected boxes to the
tracklets using a linear combination of feature cosine distances and box
overlap. But the problem of inconsistent features of an object in two different
frames still exists. In addition, when extracting features, only appearance
information is utilized, neither the location relationship nor the information
of the tracklets is considered. We present an accurate and end-to-end learning
framework for multi-object tracking, namely \textbf{TPAGT}. It re-extracts the
features of the tracklets in the current frame based on motion predicting,
which is the key to solve the problem of features inconsistent. The adaptive
graph neural network in TPAGT is adopted to fuse locations, appearance, and
historical information, and plays an important role in distinguishing different
objects. In the training phase, we propose the balanced MSE LOSS to
successfully overcome the unbalanced samples. Experiments show that our method
reaches state-of-the-art performance. It achieves 76.5\% MOTA on the MOT16
challenge and 76.2\% MOTA on the MOT17 challenge.

提出了一个多目标跟踪的准确的端到端学习框架，其中采用了运动预测重新提取了当前帧的跟踪器的特征， 基于自适应图神经网络将位置、外观和历史信息融合，并在训练中使用平衡的 MSE LOSS 来克服非平衡的样本。实验表明，该方法在 MOT16 和 MOT17 竞赛中均达到了 76.5％和 76.2％的 MOTA 的最先进水平。