Where Are You Going Next?

Abstract

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology.We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos.

TabReformer: Unsupervised Representation Learning for Erroneous Data Detection

“…For NSP, the model is pretrained with pairs of sentences, and the goal is to predict whether a given pair represents two consecutive sentences. Various implementations of transformer models [49,50] have employed the next sentence prediction objective to different applications, other than language understanding, such as location prediction [49] and cross-modality matching [51]. Therefore, the experimental evaluation (Section 4.4) includes a sensitivity analysis to estimate the effectiveness of the next tuple prediction objective to the overall performance of TabReformer.…”