On predictability of time series

Xu, Paiheng; Yin, Likang; Yue, Zhongtao; Zhou, Tao

doi:10.1016/j.physa.2019.02.006

Cited by 32 publications

(16 citation statements)

References 34 publications

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“…This technique has been extensively used to assess predictability in scenarios such as human mobility [6,8,9,20], taxi demand prediction [1], cellular network traffic [2], radio spectrum state dynamics [4], among others. Previous work also evaluated the robustness of this technique with respect to its assumptions [21] and to its mathematical details [22]. Other studies computed the limits of predictability for several temporal and spatial resolutions [9], but they did not try to explain what (besides temporal and spatial granularity) may impact predictability.…”

Section: Related Workmentioning

confidence: 99%

Deciphering Predictability Limits in Human Mobility

Teixeira

Viana

Alvim

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Human mobility has been studied from different perspectives. One approach addresses predictability, deriving theoretical limits on the accuracy that any prediction model can achieve in a given dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in the dataset, filtering out factors that depend on the specificities of the prediction method adopted. In this paper, we revisit the state-of-the-art method for estimating the predictability of a person's mobility, which, despite being widely adopted, suffers from low interpretability and disregards external factors that have been suggested to improve predictability estimation, notably the use of contextual information (e.g., weather, day of the week, and time of the day). We also conduct a thorough analysis of how this widely used method works, by looking into two different measures (one proposed by us) which are easier to understand and, as shown, capture reasonably well the effects of the original technique. Additionally, we investigate strategies to incorporate different types of contextual information into predictability estimates, and show that the benefits vary depending on the underlying prediction task. Finally, we propose and evaluate alternative estimates of predictability which, while being much easier to interpret, provide comparable results to the state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Deciphering Predictability Limits in Human Mobility

Teixeira

Viana

Alvim

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

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“…Such estimated entropy converges to the actual entropy when n reaches infinity. Importantly, when a unique substring cannot be found, then for all remaining positions 32 .…”

Section: Methodsmentioning

confidence: 99%

The impact of human mobility data scales and processing on movement predictability

Smolak

Siła-Nowicka

Delvenne

et al. 2021

Sci Rep

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Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.

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“…So the redefined upper bound is more difficult to popularize than the definition in [17]. In addition, [23] pointed out that the approximation algorithm (Lempel-Ziv estimator) of the original predictability in [17] may lead to the overestimation of predictability, and gave the inherent deviation of four different sequences.…”

Section: A Related Work On Predictabilitymentioning

confidence: 99%

Measuring the Gap Between the Maximum Predictability and Prediction Accuracy of Human Mobility

et al. 2020

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It has been claimed that human mobility is highly predictable and an upper bound of 93% predictability is achievable. However, there is a significant gap between the upper bound of predictability and the actual prediction accuracy in many data sets. This paper points out that this gap is caused by the difference between the user's actual distribution and the hypothesis in the derivation through the analysis on the upper bound of predictability. Then two statistics of the target user's mobility traces are proposed to measure this gap, whose effectiveness is validated by simulated traces and real-world data sets using five prevailing prediction models. The proposed MLP statistics can help with assessing the data quality and designing prediction algorithms. Our work makes the predictability upper bound become a more effective measure and extends the understanding of predictability research in human mobility prediction.

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On predictability of time series

Cited by 32 publications

References 34 publications

Deciphering Predictability Limits in Human Mobility

Deciphering Predictability Limits in Human Mobility

The impact of human mobility data scales and processing on movement predictability

Measuring the Gap Between the Maximum Predictability and Prediction Accuracy of Human Mobility

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