Traffic prediction in a bike-sharing system based on hierarchical time series

2016 ◽  
Author(s):  
Yexin Li
Keyword(s):  
Time Series ◽  
Traffic Prediction ◽  
Bike Sharing

MetaTP

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Weida Zhong ◽  
Qiuling Suo ◽  
Abhishek Gupta ◽  
Xiaowei Jia ◽  
Chunming Qiao ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Modern Society ◽  
Training Data ◽  
Traffic Prediction ◽  
Temporal Prediction ◽  
Reference Space ◽  
Meta Learning ◽  
Real World Datasets

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

scholarly journals OPTIMISING THE SMOOTHNESS AND ACCURACY OF MOVING AVERAGE FOR STOCK PRICE DATA

2018 ◽  
Vol 24 (3) ◽  
pp. 984-1003 ◽  
Author(s):  
Aistis RAUDYS ◽  
Židrina PABARŠKAITĖ
Keyword(s):  
Time Series ◽  
Real World ◽  
Stock Price ◽  
Weather Forecasting ◽  
Moving Average ◽  
New Method ◽  
Traffic Prediction ◽  
Real World Data ◽  
Price Series ◽  
Unseen Data

Smoothing time series allows removing noise. Moving averages are used in finance to smooth stock price series and forecast trend direction. We propose optimised custom moving average that is the most suitable for stock time series smoothing. Suitability criteria are defined by smoothness and accuracy. Previous research focused only on one of the two criteria in isolation. We define this as multi-criteria Pareto optimisation problem and compare the proposed method to the five most popular moving average methods on synthetic and real world stock data. The comparison was performed using unseen data. The new method outperforms other methods in 99.5% of cases on synthetic and in 91% on real world data. The method allows better time series smoothing with the same level of accuracy as traditional methods, or better accuracy with the same smoothness. Weights optimised on one stock are very similar to weights optimised for any other stock and can be used interchangeably. Traders can use the new method to detect trends earlier and increase the profitability of their strategies. The concept is also applicable to sensors, weather forecasting, and traffic prediction where both the smoothness and accuracy of the filtered signal are important.

scholarly journals Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1305
Author(s):  
Shenghan Zhou ◽  
Bang Chen ◽  
Houxiang Liu ◽  
Xinpeng Ji ◽  
Chaofan Wei ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Moving Average ◽  
Traffic Prediction ◽  
Data Sets ◽  
Prediction Modeling ◽  
Autoregressive Integrated Moving Average ◽  
Smart Transportation ◽  
Characteristics Analysis ◽  
Operational Data

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

Multi-dimensional Prediction Model for Cell Traffic in City Scale

2020 ◽  
pp. 2150010
Author(s):  
Hong Wang ◽  
Liqun Wang ◽  
Shufang Zhao ◽  
Xiuming Yue
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Base Stations ◽  
Series Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Spatial Dimensions ◽  
Temporal And Spatial

Traffic prediction is a classical time series prediction which has been investigated in different domains, but most existing models are proposed based on limited time or spatial scale. Mobile cellular network traffic prediction is of paramount importance for quality-of-service (QoS) and power management of the cellular base stations, especially in the 5G era. Through the statistical analysis of the real historical traffic data obtained in a city scale spanning across multiple months, this paper makes an in-depth study of the temporal characteristics and behavior rules of the model data traffic. Considering that the time series data show different changing rules under the different time dimensions, spatial dimensions and independent dimensions, a multi-dimensional recurrent neural network (MDRNN) prediction model is established to predict the future cell traffic volume over various temporal and spatial dimensions. The data of this paper are trained and tested over real data of a city, and the granularity of the proposed prediction model can be drilled down to the cell level. Compared with the traditional trend fitting method, the proposed model achieves mean absolute percentage error (MAPE) reduction of 6.56%, and provides guidance for energy efficiency optimization and power consumption reduction of base stations in various temporal and spatial dimensions.

Comparative study of time series model in traffic prediction of GPRS cells

2010 ◽  
Vol 30 (4) ◽  
pp. 884-887 ◽  
Author(s):  
Xin ZHOU ◽  
Jin ZHANG ◽  
Yan-ke ZHAO ◽  
Ru-long WANG
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Time Series Model ◽  
Traffic Prediction
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