"temporal similarity" is a correct and commonly used phrase in written English.
It refers to similarities or comparisons between different points in time. It can be used in various contexts, such as in academic writing, scientific research, or everyday language. Example: "The study found a high temporal similarity between the patterns of climate change in the 20th and 21st centuries."
To inform our predictions we utilize locality, the past demand trends of venues in our neighborhood, and temporal similarity, the demand trends of temporally synchronous areas of the city.
Further, we see that temporal similarity can also be used to improve predictions; this may be because similar venues could have synchronous peaks in demand following a similar impact from real-world events.
Figure 4 Temporal similarity of wards.
Therefore, this paper proposes a non-classical approach to measure temporal similarity of cases which are heterogeneous temporal event sequences.
In domains in which time plays a relevant role, CBR systems require good temporal similarity measures to compare cases.
While consecutive frames in a video have lots of temporal similarity, it is hard to find temporal similarity in part of an image for several reasons such as occlusion and homogenous background.
Cooper et al. [30] presented an automatic temporal similarity-based method using EXIF data.
In the case of video, the proposed recovery method is able to exploit both spatial and temporal similarities.
In this section, we describe how our approach to the prediction of visitation patterns to new venues harnesses temporal similarities of urban neighborhoods.
The cross-covariance function is at the heart of this analysis, and this function quantifies the temporal similarities of the recorded waveforms.
This method explores the temporal similarities and dissimilarities among relationships at different stages of aging in the mouse hippocampus.
