Prophet model and Gaussian process regression based user traffic prediction in wireless networks

Yu, Li; Ma, Zhimin; Pan, Zhiwen; Liu, Nan; You, Xiaohu

doi:10.1007/s11432-019-2695-6

Cited by 26 publications

(12 citation statements)

References 17 publications

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“…For GPR, 32 essentially all parameters involved could be estimated directly from the data by the method of maximum likelihood. (ii) Hybrid methods, including Wavelet-PG, 24 p-TNN-GP (the periodic component is predicted by GPR and the burst component by the Prophet model), and p-TNN-GG (both components are predicted by GPR). The Wavelet-PG 24 first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…(ii) Hybrid methods, including Wavelet-PG, 24 p-TNN-GP (the periodic component is predicted by GPR and the burst component by the Prophet model), and p-TNN-GG (both components are predicted by GPR). The Wavelet-PG 24 first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. Then the Prophet model and Gaussian process regression are applied to predict the two components, respectively.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…22 Due to the manifold statistic characteristics of per-user cellular traffic, such as long-range dependence (LRD), multifractal, and chaotic feature, a hybrid model could achieve more accurate traffic prediction than a single one. Existing hybrid (decomposition) models 23,24 for per-user traffic prediction decompose per-user traffic into separate components and are built on the assumption that the original traffic data are complete. Unfortunately, a more common scenario in practice is that the original traffic data are usually missing and incomplete.…”

Section: Introductionmentioning

confidence: 99%

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Individual traffic prediction in cellular networks based on tensor completion

Liu

et al. 2021

Int J Communication

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SummaryAccurate individual (per‐user) traffic prediction in cellular networks is considered a critical capability for the system performance improvement in terms of dynamic bandwidth allocation and network optimization. However, existing methods have limitations in capturing the characteristics of individual traffic, because the observed traffic data are usually incomplete and traffic consumption patterns significantly differ among users. In this paper, to fully exploit the inherent temporal‐spatial correlations of individual traffic data, this work models the traffic data as a three‐dimensional traffic tensor. Different from the “three‐step” strategies (i.e., “data processing + decomposition + prediction”) in the existing methods, we propose a novel tensor completion (TC)‐based “two‐step” strategy, that is, “data processing & decomposition + prediction” for individual traffic prediction, which can recover and decompose the traffic data simultaneously. Furthermore, an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to solve the resulting model. To the best of our knowledge, this is the first work to apply the TC‐based “two‐step” strategy for individual traffic prediction in cellular networks. Experiments conducted on a real cellular traffic dataset empirically validate the superiority of the proposed method.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Individual traffic prediction in cellular networks based on tensor completion

Liu

et al. 2021

Int J Communication

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“…2N parameters β = [a1, b1, …aN, bN] T are estimated for the seasonality. The seasonality matrix is constructed for each value of t in past and future data (Li et al, 2020). The seasonality component is shown in equation ( 8): S(t) = X(t) β (8)…”

Section: Seasonality Modelmentioning

confidence: 99%

Predicting Customer Behavior Using Prophet Algorithm In A Real Time Series Dataset

Lico

Enesi

Jaiswal

2021

ESJ

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Customer Relationship Management is important in analyzing business performance. Predicting customer buying behavior enables the business to better address their customers and enhance service level and overall profit. This paper focuses on proposing a model that predicts future period sales in a real retail department store with low prediction error rate, and it also discovers the main sales trends over time. A model based on the Prophet algorithm is implemented and modified according to different parameters in order to lower the prediction error. The modifications consisted of the insertion of a new seasonality pattern, changes in the Fourier order of the existing and the new seasonality pattern, inclusion of the holiday data, and parameterizing its impact. The performance of the standard and modified model is evaluated in terms of the MAE (mean absolute error) and MAPE (mean absolute percentage error). The standard and the modified model were tested on a real dataset consisting of the sales between 2011-2019 in a department store of a shopping center in Albania. Implementation results show that the MAE in sales prediction for the modified model is reduced, while the MAPE in sales prediction for the modified model was measured for prediction periods. The implementation results indicate a comparable or evenbetter performance than the standard model. different

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“…Zhao and others used a model that combined least squares and radial basis function neural network to predict sea level anomaly series in offshore China, and the reliability of the model for short-term predictions was demonstrated, and the accuracy reached 0.65 cm [11]. Among the methods selected in this article, SARIMA is widely used in epidemiological prediction [12] and the Prophet model has good performance in user traffic prediction [13]. As neural network models, LSTM and RBF have certain application value in rainfall and river flow predictions [14,15].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Liu

Jiao

Sun

et al. 2021

Scientific Programming

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The South China Sea is China’s largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea.

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Prophet model and Gaussian process regression based user traffic prediction in wireless networks

Cited by 26 publications

References 17 publications

Individual traffic prediction in cellular networks based on tensor completion

Individual traffic prediction in cellular networks based on tensor completion

Predicting Customer Behavior Using Prophet Algorithm In A Real Time Series Dataset

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

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