Long-term time-series forecasting remains challenging due to the difficulty
in capturing long-term dependencies, achieving linear scalability, and
maintaining computational efficiency. We introduce TimeMachine, an innovative
model that leverages Mamba, a state-space model, to capture long-term
dependencies in multivariate time series data while maintaining linear
scalability and small memory footprints. TimeMachine exploits the unique
properties of time series data to produce salient contextual cues at
multi-scales and leverage an innovative integrated quadruple-Mamba architecture
to unify the handling of channel-mixing and channel-independence situations,
thus enabling effective selection of contents for prediction against global and
local contexts at different scales. Experimentally, TimeMachine achieves
superior performance in prediction accuracy, scalability, and memory
efficiency, as extensively validated using benchmark datasets. Code
availability: this https URL

TimeMachine 是一种创新的模型，利用 Mamba 作为状态空间模型来捕捉多变量时间序列数据中的长期依赖关系，同时具有线性可扩展性和小内存占用。TimeMachine 利用时间序列数据的独特属性，在多个尺度上生成显著的上下文提示，并利用一种创新的集成四重 - Mamba 架构统一处理混合通道和独立通道情况，从而在不同尺度上以全局和局部上下文的方式对内容进行有效选择以进行预测。实验结果表明，TimeMachine 在预测准确性、可扩展性和内存效率方面表现出优异的性能，已在基准数据集上进行了广泛验证。

时间机器：长期预测的时间序列价值等于 4 条眼镜蛇

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

We present a method called TIMEMACHINE to generate a timeline of events and
relations for entities in a knowledge base. For example for an actor, such a
timeline should show the most important professional and personal milestones
and relationships such as works, awards, collaborations, and family
relationships. We develop three orthogonal timeline quality criteria that an
ideal timeline should satisfy: (1) it shows events that are relevant to the
entity; (2) it shows events that are temporally diverse, so they distribute
along the time axis, avoiding visual crowding and allowing for easy user
interaction, such as zooming in and out; and (3) it shows events that are
content diverse, so they contain many different types of events (e.g., for an
actor, it should show movies and marriages and awards, not just movies). We
present an algorithm to generate such timelines for a given time period and
screen size, based on submodular optimization and web-co-occurrence statistics
with provable performance guarantees. A series of user studies using Mechanical
Turk shows that all three quality criteria are crucial to produce quality
timelines and that our algorithm significantly outperforms various baseline and
state-of-the-art methods.

本文提出了一种名为 TIMEMACHINE 的方法，用于生成知识库中实体的事件和关系时间轴，并发展了三个正交的时间轴质量标准来满足理想的时间轴时必须满足的条件，我们提出了一种基于子模不优化和 Web 协同出现统计的算法来为特定时间段和屏幕大小生成这样的时间轴。