BigDataFr recommends: Learning to Predict with Big Data

[…] Subjects:

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. One of the issues researchers face with such data is the stationarity assumption. This poses several challenges in how to quantify uncertainty and bias. While there are recent advances in forecasting techniques for highly granular temporal data, little attention is given to segmenting the time series and finding homogeneous patterns. In this paper, it is proposed to estimate behavioral profiles of individuals’ activities over time using Gaussian Process based models. In particular, the aim is to investigate how individuals or groups may be clustered according to the model parameters. […]

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By Anastasia Ushakova, Slava J. Mikhaylov
Source: arxiv.org