scholarly journals Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

2021 ◽  
Author(s):  
Tetsuya Yamada ◽  
Shoi Shi
Keyword(s):  
Time Series ◽  
Infectious Diseases ◽  
Time Series Data ◽  
Human Mobility ◽  
Emerging Infectious Diseases ◽  
Human Movement ◽  
Data Driven ◽  
Disease Spread ◽  
Series Data ◽  
Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

scholarly journals GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

2021 ◽  
Vol 5 (5) ◽  
pp. 619-635
Author(s):  
Harya Widiputra
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Human Mobility ◽  
Positive Association ◽  
Human Movement ◽  
Daily Basis ◽  
Series Data ◽  
Data Prediction ◽  
The Future

The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF

scholarly journals Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2812 ◽  
Author(s):  
Jing Yang ◽  
Yizhong Sun ◽  
Bowen Shang ◽  
Lei Wang ◽  
Jie Zhu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Human Mobility ◽  
Spatiotemporal Patterns ◽  
Classification Model ◽  
Series Data ◽  
Similarity Measurement ◽  
Mobility Patterns ◽  
Point Data ◽  
Destination Point

With the availability of large geospatial datasets, the study of collective human mobility spatiotemporal patterns provides a new way to explore urban spatial environments from the perspective of residents. In this paper, we constructed a classification model for mobility patterns that is suitable for taxi OD (Origin-Destination) point data, and it is comprised of three parts. First, a new aggregate unit, which uses a road intersection as the constraint condition, is designed for the analysis of the taxi OD point data. Second, the time series similarity measurement is improved by adding a normalization procedure and time windows to address the particular characteristics of the taxi time series data. Finally, the DBSCAN algorithm is used to classify the time series into different mobility patterns based on a proximity index that is calculated using the improved similarity measurement. In addition, we used the random forest algorithm to establish a correlation model between the mobility patterns and the regional functional characteristics. Based on the taxi OD point data from Nanjing, we delimited seven mobility patterns and illustrated that the regional functions have obvious driving effects on these mobility patterns. These findings are applicable to urban planning, traffic management and planning, and land use analyses in the future.

scholarly journals Partial Correlation-Based Attention for Multivariate Time Series Forecasting

2020 ◽  
Vol 34 (10) ◽  
pp. 13720-13721
Author(s):  
Won Kyung Lee
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Series Data ◽  
Time Lags ◽  
Dependency Structure ◽  
Agnostic Learning

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

scholarly journals A Hybrid Learning Approach to Prognostics and Health Management Applied to Military Ground Vehicles Using Time-Series and Maintenance Event Data

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
W Glenn Bond ◽  
Haley Dozier ◽  
Thomas L Arnold ◽  
Michael Y Lam ◽  
Quyen T Dong ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analytics ◽  
High Performance ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Event Data ◽  
Ground Vehicles ◽  
Operational Data ◽  
Operational Time

Attempts to leverage operational time-series data in Condition Based Maintenance (CBM) approaches to optimize the life cycle management and Reliability, Availability, and Maintainability (RAM) of military vehicles have encountered several obstacles over decades of data collection. These obstacles have beset similar approaches on civilian ground vehicles, as well as on aircraft and other complex systems. Analysis of operational data is critical because it represents a continuous recording of the state of the system. Applying rudimentary data analytics to operational data can provide insights like fuel usage patterns or observed reliability of one vehicle or even a fleet. Monitoring trends and analyzing patterns in this data over time, however, can provide insight into the health of a vehicle, a complex system, or a fleet, predicting mean time to failure or compiling logistic or life cycle needs. Such High-Performance Data Analytics (HPDA) on operational time-series datasets has been historically difficult due to the large amount of data gathered from vehicle sensors, the lack of association between clusters observed in the data and failures or unscheduled maintenance events, and the deficiency of unsupervised learning techniques for time-series data. We present an HPDA environment and a method of discovering patterns in vehicle operational data that determines models for predicting the likelihood of imminent failure, referred to as Parameter-Based Indicators (PBIs). Our method is a data-driven approach that uses both time-series and relational maintenance data. This hybrid approach combines both supervised and unsupervised machine learning and data analytic techniques to correlate labeled, relational maintenance event data with unlabeled operational time-series data utilizing the DoD High Performance Computing (HPC) capabilities at the U.S. Army Engineer Research and Development Center. In leveraging both time-series and relational data, we demonstrate a means of fast, purely data-driven model creation that is more broadly applicable and requires less a priori information than physics informed, data-driven models. By blending these approaches, this system will be able to relate some lifecycle management goals through the workflow to generate specific PBIs that will predict failures or highlight appropriate areas of concern in individual or collective vehicle histories.

scholarly journals Energy Demand Relationship: Theory and Empirical Application. A Short Note

2020 ◽  
Author(s):  
Fakhri J. Hasanov ◽  
Jeyhun L. Mikayilov
Keyword(s):  
Time Series ◽  
Production Function ◽  
Energy Demand ◽  
Demand Function ◽  
Time Series Data ◽  
Short Note ◽  
Data Driven ◽  
Series Data ◽  
Industrial Energy ◽  
Empirical Analyses

In this short note, the described step-by-step derivations of the industrial energy demand function from the production function framework and provided researchers with two specifications. Then we applied these theoretical specifications to the time series data as empirical analysis. We concluded that theories should be considered at the beginning of the empirical analyses but the data also should be allowed to speak freely. Hence, the main suggestion of this short note is that it would be a better strategy to consider the combination of theory-driven and data-driven approaches in the empirical analyses.

Time series prediction using machine learning: a case of Bitcoin returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irfan Haider Shakri
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Predictive Ability ◽  
Absolute Error ◽  
Data Driven ◽  
Series Data ◽  
Policy Uncertainty ◽  
Content Type

Purpose The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms. Design/methodology/approach The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking. Findings Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns. Originality/value This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

scholarly journals Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

2021 ◽  
Vol 12 (2) ◽  
pp. 1-21
Author(s):  
Zijian Li ◽  
Ruichu Cai ◽  
Hong Wei Ng ◽  
Marianne Winslett ◽  
Tom Z. J. Fu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Domain Adaptation ◽  
Mechanical Systems ◽  
Lessons Learned ◽  
Data Driven ◽  
Series Data ◽  
Causal Mechanism ◽  
Model Generalization ◽  
High Dynamics

Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adaptivity to high dynamics and aging equipment, they are usually hungry for massive labels, mostly contributed by human engineers at a high cost. Fortunately, domain adaptation enhances the model generalization by utilizing the labeled source data and the unlabeled target data. However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, since they assume that the conditional distributions are equal. This assumption works well in the static data but is inapplicable for the time series data. Even the first-order Markov dependence assumption requires the dependence between any two consecutive time steps. In this article, we assume that the causal mechanism is invariant and present our Causal Mechanism Transfer Network (CMTN) for time series domain adaptation. By capturing causal mechanisms of time series data, CMTN allows the data-driven models to exploit existing data and labels from similar systems, such that the resulting model on a new system is highly reliable even with limited data. We report our empirical results and lessons learned from two real-world case studies, on chiller plant energy optimization and boiler fault detection, which outperform the existing state-of-the-art method.

scholarly journals A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4466
Author(s):  
Li Guo ◽  
Runze Li ◽  
Bin Jiang
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Fault Prediction ◽  
Data Driven ◽  
Series Data ◽  
Sequence Prediction ◽  
Long Time Series ◽  
Long Time

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

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