scholarly journals Prediction of Repeat Customers on E-Commerce Platform Based on Blockchain

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huibing Zhang ◽  
Junchao Dong
Keyword(s):  
Prediction Model ◽  
Ensemble Learning ◽  
Information Fusion ◽  
Prediction Accuracy ◽  
Feature Engineering ◽  
Learning Models ◽  
Information Dimension ◽  
Model Based ◽  
Repeat Purchase ◽  
The Impact

In recent years, blockchain has substantially enhanced the credibility of e-commerce platforms for users. The prediction accuracy of the repeat purchase behaviour of e-commerce users directly affects the impact of precision marketing by merchants. The existing ensemble learning models have low prediction accuracy when the purchase behaviour sample is unbalanced and the information dimension of feature engineering is single. To overcome this problem, an ensemble learning prediction model based on multisource information fusion is proposed. Tests on the Tmall dataset showed that the accuracy and AUC values of the model reached 91.28% and 70.53%, respectively.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

scholarly journals BDS Satellite Clock Prediction Considering Periodic Variations

2021 ◽  
Vol 13 (20) ◽  
pp. 4058
Author(s):  
Lin Zhao ◽  
Nan Li ◽  
Hui Li ◽  
Renlong Wang ◽  
Menghao Li
Keyword(s):  
Fourier Transform ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Analysis Model ◽  
Satellite Clock ◽  
Periodic Term ◽  
Periodic Noise ◽  
Clock Prediction ◽  
The Impact ◽  
Over Time

The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.

scholarly journals Harvest prediction model based on public data for large regions

2015 ◽  
Vol 37 (1) ◽  
pp. 135-140
Author(s):  
Andrius Zuoza ◽  
Aurelijus Kazys Zuoza ◽  
Audrius Gargasas
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Environmental Variables ◽  
Prediction Accuracy ◽  
The Public ◽  
Model Based ◽  
Public Data

This article describe harvest prediction model for the country or for the big region on the public available data. In the article are analysed impact of main fertilizers component and environmental variables to the grain harvest The aim of the article was to create regression model, which best describes grain harvest prediction on public (free) available data. Created final regression model explain 78% (R2) of the variation in the harvest result. Presented model show, that prediction accuracy significantly increase if environmental variables are added. Prediction accuracy (RMSE) of the final regression model was 3,89. All calculation was made on the example of the Germany.

scholarly journals The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

A prediction model for chemotherapy-associated thrombocytopenia in cancer patients

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 8616-8616 ◽  
Author(s):  
N. M. Kuderer ◽  
C. W. Francis ◽  
J. Crawford ◽  
D. C. Dale ◽  
D. A. Wolff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Likelihood Ratio ◽  
Test Performance ◽  
Dose Intensity ◽  
High Specificity ◽  
Performance Characteristics ◽  
Multivariate Logistic Regression Model ◽  
Model Based ◽  
The Impact

8616 Background: Thrombocytopenia (TP) can lead to serious complications, however, little is known about the incidence and risk factors for chemotherapy-associated TP. A prospective, nationwide cohort study was undertaken to better define the impact of TP in cancer treatment. Methods: 2,842 patients with cancer of the breast, lung, colon, ovary or lymphoma initiating a new chemotherapy regimen have been prospectively enrolled at 115 randomly selected US community oncology practices between 2002 and 2005. Risk factors for chemotherapy-associated TP were identified, a multivariate logistic regression model based on pretreatment characteristics was developed, and test performance characteristics were estimated. Results: Over a median of 3 cycles of chemotherapy, minimum recorded platelet counts were: ≥150K in 53% of patients; 100–150K in 26%; 75–100K in 8%; 50–75K in 6% and <50K in 7%. Significant independent predictive factors for platelets <75K include type of cancer (P<.0001), type of chemotherapy including gemcitabine-based (P<.0001), anthracycline-based (P<.0001) and platinum-based (P<.0001) regimens, prior chemotherapy (P<.0001) or surgery (P=.005), age (P=.015), Caucasian ethnicity (P=.022), body surface area (P=.0001), planned relative dose intensity ≥85% (P=.082), diabetes (P=.018), pulmonary disease (P=.011), abnormal baseline platelets (P<.0001), hematocrit (P=0.030), alkaline phosphatase (P=.072) or albumin (P=.017). Model fit was good (Chi-square, P<.0001), R2 = 0.735 and c-statistic = 0.816 [95% CI: 0.792–0.840, P<.0001]. Model test performance characteristics [95% CI] at a ≥20% risk of TP include: sensitivity 56% [51–61]; specificity 88% [87–89]; likelihood ratio positive 4.63 [4.02–5.33]; likelihood ratio negative 0.50 [0.45–0.57]; and diagnostic odds ratio 9.22 [7.23–11.75]. Validation of the model is underway. Conclusions: This prediction model based on pretreatment factors identifies with high specificity patients at risk for clinically important chemotherapy-associated thrombocytopenia early in the treatment course. It may provide a valuable tool for guiding chemotherapy and new supportive care measures. [Table: see text]

Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

2013 ◽  
Vol 300-301 ◽  
pp. 189-194 ◽  
Author(s):  
Yu Sun ◽  
Ling Ling Li ◽  
Xiao Song Huang ◽  
Chao Ying Duan
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Neural Network Prediction ◽  
Random Fluctuations ◽  
The Impact

To avoid the impact which is caused by the characteristics of the random fluctuations of the wind speed to grid-connected wind power generation system, accurately prediction of short-term wind speed is needed. This paper designed a combination prediction model which used the theories of wavelet transformation and support vector machine (SVM). This improved the model’s prediction accuracy through the method of achiving change character in wind speed sequences in different scales by wavelet transform and optimizing the parameters of support vector machines through the improved particle swarm algorithm. The model showed great generalization ability and high prediction accuracy through the experiment. The lowest root-mean-square error of 200 samples was up to 0.0932 and the model’s effect was much stronger than the BP neural network prediction model. It provided an effective method for predicting wind speed.

Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

2021 ◽  
pp. 1-14
Author(s):  
Jia-Nian Zhu ◽  
Xu-Chong Liu ◽  
Chong Liu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based ◽  
Whale Optimization ◽  
High Prediction ◽  
Best Parameters ◽  
Better Than

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

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