scholarly journals Spatio-Temporal Attention based Recurrent Neural Network for Next Location Prediction

2018 ◽  
Author(s):  
Basmah Altaf ◽  
Lu Yu ◽  
Xiangliang Zhang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Location Prediction ◽  
Temporal Attention ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis

2018 ◽  
Author(s):  
Guolei Yang ◽  
Ying Cai ◽  
Chandan K Reddy
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Recurrent Neural Network ◽  
Censored Regression ◽  
Event Time ◽  
Time Prediction ◽  
Spatio Temporal ◽  
Latent Representations ◽  
Real World Datasets ◽  
Prediction Techniques

We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i.e., users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.

scholarly journals Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection

2020 ◽  
Vol 34 (01) ◽  
pp. 362-369 ◽  
Author(s):  
Dawei Cheng ◽  
Sheng Xiang ◽  
Chencheng Shang ◽  
Yiyi Zhang ◽  
Fangzhou Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Domain Knowledge ◽  
Empirical Studies ◽  
Fraud Detection ◽  
Temporal Attention ◽  
Credit Card Fraud ◽  
Domain Experts ◽  
Spatio Temporal ◽  
Better Than

Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns.

Disaggregate Short-Term Location Prediction Based on Recurrent Neural Network and an Agent-Based Platform

2019 ◽  
Vol 2673 (8) ◽  
pp. 657-668
Author(s):  
Yanjie Dong ◽  
John Polak ◽  
Aruna Sivakumar ◽  
Fangce Guo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Simulated Data ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Agent Based ◽  
Individual Level ◽  
Data Limitations

With the growing popularity of mobile and sensory devices, there has been a strong research interest in short-term disaggregate-level location prediction. Such predictive models have huge application potential in several sectors to change and improve people’s daily life and experience. Existing methods in this research stream have mainly focused on the prediction of sequence of location, with valuable temporal information overlooked. In addition, data limitations have constrained the development and understanding from different algorithms. In this paper, the authors propose a recurrent neural network-based method (RNN and LSTM, long short-term memory) for the next and future location prediction. This model predicts the sequence in time, thus it can predict both when and where an individual will be in the future and the duration of the stay at each location. The predictive model is developed based on an agent-based simulation platform that can produce realistic spatial-temporal trajectory data at the individual level. Analysis of the simulated data has shown that RNN and LSTM are capable of predicting future locations with better results than other comparative methods, especially for agents with high location variability. Online prediction with true location information fed into the model later in the day would greatly improve the predicted results. However, significant variations can be observed at the zonal level, with all methods performing much better on frequently visited locations than less visited locations or irregular visits.

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