Forecasting Website Traffic Using Prophet Time Series Model

Web traffic is the amount of data sent and received by visitors to a website and it has been the largest portion of Internet traffic. Internet traffic flow prediction heavily depends on historical and real-time traffic data collected from various internet flow monitoring sources. With the widespread traditional traffic sensors and new emerging traffic sensor technologies, traffic data are exploding, and we have entered the era of big data internet traffic. Internet traffic management and control driven by big data is becoming a new trend. Although there have been already many internet traffic flow prediction systems and models, most of which use shallow traffic models and are still somewhat unsatisfying. This inspires us to reconsider the internet traffic flow prediction model based on deep architecture models with such rich amount of internet traffic data. ARIMA is a existing forecasting technique that predicts the future values of a series based entirely on its own inertia. Existing traffic flow prediction methods mainly use simple traffic prediction models and are still unsatisfying for many real-world applications. Now we proposed the prophet time series model to forecasting website traffic.

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Poster abstract: Traffic flow prediction with big data: A deep learning based time series model

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) ◽

10.1109/infcomw.2017.8116535 ◽

2017 ◽

Cited By ~ 11

Author(s):

Yuanfang Chen ◽

Lei Shu ◽

Lei Wang

Keyword(s):

Time Series ◽

Big Data ◽

Deep Learning ◽

Traffic Flow ◽

Time Series Model ◽

Poster Abstract ◽

Traffic Flow Prediction ◽

Flow Prediction

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FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2020.p0829 ◽

2020 ◽

Vol 24 (7) ◽

pp. 829-836

Author(s):

Di Yang ◽

◽

Ningjia Qiu ◽

Peng Wang ◽

Huamin Yang

Keyword(s):

Traffic Flow ◽

Prediction Models ◽

Road Traffic ◽

Transportation Systems ◽

Natural Order ◽

Traffic Data ◽

Traffic Flow Prediction ◽

Task Learning ◽

Flow Prediction ◽

Highly Correlated

Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and pairwise differences between neighboring coefficients, has been widely used to analyze highly correlated features with a natural order, as is the case with traffic flow. It denoises data by encouraging both sparsity of coefficients and their differences, and estimates the coefficients of highly correlated variables to be equal to each other. However, for traffic data, the same coefficients will lead to overexpression of features, and losing the trend of time series of traffic flow. In this work, we propose a Fused Ridge multi-task learning (FR-MTL) model for multi-road traffic flow prediction. It introduces Fused Ridge for traffic data denoising, imposes penalty on L2-norm of the coefficients and their differences. The penalty of L2-norm proportionally shrinks coefficients, and generates smooth coefficient vectors with non-sparsity. It has both capability of trend preservation and denoising. In addition, we jointly consider multi-task learning (MTL) for training shared spatiotemporal information among traffic roads. The experiments on real traffic data show the advantages of the proposed model over other four regularized baseline models, and on traffic data with Gaussian noise and missing data, the FR-MTL model demonstrates potential and promising capability with satisfying accuracy and effectiveness.

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Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

Journal of Advanced Transportation ◽

10.1155/2018/3876841 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Yi Zhao ◽

Satish V. Ukkusuri ◽

Jian Lu

Keyword(s):

Dimension Reduction ◽

Traffic Flow ◽

Reduction Method ◽

Prediction Models ◽

Urban Traffic ◽

Dimension Reduction Method ◽

Short Term ◽

Urban Road ◽

Traffic Flow Prediction ◽

Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

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Traffic Flow Prediction of Chaos Time Series by Using Subtractive Clustering for Fuzzy Neural Network Modeling

2008 Second International Symposium on Intelligent Information Technology Application ◽

10.1109/iita.2008.50 ◽

2008 ◽

Cited By ~ 9

Author(s):

Ming-bao Pang ◽

Xin-ping Zhao

Keyword(s):

Neural Network ◽

Time Series ◽

Traffic Flow ◽

Fuzzy Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Subtractive Clustering ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Fuzzy Neural

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Journal of Advanced Transportation ◽

10.1155/2019/4145353 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 37

Author(s):

Xianglong Luo ◽

Danyang Li ◽

Yu Yang ◽

Shengrui Zhang

Keyword(s):

Support Vector Regression ◽

Traffic Flow ◽

Prediction Models ◽

Short Term Memory ◽

Transportation Systems ◽

Support Vector ◽

K Nearest Neighbor ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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