论文里的好句子（8）

1.

In these real-world applications, one pressing demand is to extend the forecast time into the far future, which is quite meaningful for the long-term planning and early warning.

在这些实际应用中，一个迫切的需求是将预测时间延伸到遥远的未来，这对于长期规划和预警具有重要意义。

2.

Recent deep forecasting models have achieved great progress, especially the Transformer-based models.

Benefiting from the self-attention mechanism, Transformers obtain great advantage in modeling

long-term dependencies for sequential data, which enables more powerful big models.

近年来，深度预测模型取得了很大的进展，尤其是基于Transformer的模型。

得益于自关注机制，Transformers在为序列数据建模长期依赖关系方面获得了巨大优势，从而实现了更强大的大模型。

3.

computationally prohibitive for long-term forecasting because of the quadratic complexity of sequence length

由于序列长度的二次复杂性，长期预报在计算上受到限制

4.

We observe that the sub-series at the same phase position among periods often present similar temporal processes.

Thus, we try to construct a series-level connection based on the process similarity derived by series periodicity.

我们观察到，周期之间处于相同相位位置的子序列往往呈现出相似的时间过程。

因此，我们尝试建立一个基于过程相似性的串联连接，过程相似性由串联周期性决定。

5.

progressively decompose the hidden series throughout the whole forecasting process

在整个预测过程中逐步分解隐藏序列

6.

However, such pre-processing is limited by the plain decomposition effect of historical series and overlooks the hierarchical interaction between the underlying patterns of series in the long-term future.

This paper takes the decomposition idea from a new progressive dimension.

本文从一个新的递进维度引入了分解思想。

7.

As a standard method in time series analysis, time series decomposition deconstructs a time series into several components, each representing one of the underlying categories of patterns that are more predictable.

deconstruct 解构颠覆消解函数

8.

By embedding our proposed decomposition blocks as the inner operators, Autoformer can progressively separate the long-term trend information from predicted hidden variables.

This design allows our model to alternately decompose and refine the intermediate results during the forecasting procedure.

9.

We observe that the sub-series at the same phase position among periods often present similar temporal processes.

Thus, we try to construct a series-level connection based on the process similarity derived by series periodicity.

10.

This common usage limits the capabilities of decomposition and overlooks the potential future interactions among decomposed components.

11.

In these real-world applications, one pressing demand is to extend the forecast time into the far future, which is quite meaningful for the long-term planning and early warning.