Group Daily Arrival Prediction Based on A Hybrid Model Combing Autoregressive Integrated Moving Average Model and Back Propagation Neural Network

2020 ◽  
Author(s):  
Xiaoling Zhu ◽  
Xi Zhang ◽  
Ge Shi ◽  
Yashen Wang
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

scholarly journals Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian

2021 ◽  
Vol 10 (3) ◽  
pp. 707
Author(s):  
Chee Ka Chin ◽  
Dayang Azra binti Awang Mat ◽  
Abdulrazak Yahya Saleh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cell Carcinoma ◽  
Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Neural Network Algorithm

Skin cancer is a widely spreading cause of mortality among the people specifically living on or near the equatorial belt. Early detection of skin cancer significantly improves the recovery prevalence and the chance of surviving. Without the assist of computer-aided decision (CAD) system, skin cancer classification is the challenging task for the dermatologist to differentiate the type of skin cancer and provide the suitable treatment. Recently, the development of machine learning and pretrained deep neural network (DNN) shows the tremendous performance in image classification task which also provide the promising performance in medical field. However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. This paper focuses on the classification of skin cancer to identify whether it is basal cell carcinoma, melanoma or squamous cell carcinoma by using the development of hybrid convolutional neural network algorithm and autoregressive integrated moving average model (CNN-ARIMA). The CNNARIMA model was trained and found to produce the best accuracy of 92.25%.

scholarly journals Machine Learning and Time Series Models for VNQ Market Predictions

2021 ◽  
pp. 29-44
Author(s):  
Yu-Min Lian ◽  
Chia-Hsuan Li ◽  
Yi-Hsuan Wei
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Real Estate ◽  
Multiple Linear Regression ◽  
Moving Average ◽  
Back Propagation ◽  
The United States ◽  
Back Propagation Neural Network ◽  
Autoregressive Integrated Moving Average ◽  
Input Variables

Abstract This study compares the price predictions of the Vanguard real estate exchange-traded fund (ETF) (VNQ) using the back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models. The input variables for BPNN include the past 3-day closing prices, daily trading volume, MA5, MA20, the S&P 500 index, the United States (US) dollar index, volatility index, 5-year treasury yields, and 10-year treasury yields. In addition, variable reduction is based on multiple linear regression (MLR). Mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to measure the prediction error between the actual closing price and the models’ forecasted price. The training set covers the period between January 1, 2015 and March 31, 2020, and the forecasting set covers the period from April 1, 2020 to June 30, 2020. The empirical results reveal that the BPNN model’s predictive ability is superior to the ARIMA model’s. The predictive accuracy of BPNN with one hidden layer is better than with two hidden layers. Our findings provide crucial market factors as input variables for BPNN that might inspire investors in VNQ markets. JEL classification numbers: C32, C45, C53, G17. Keywords: Vanguard real estate ETF (VNQ), Back propagation neural network (BPNN), Autoregressive integrated moving average (ARIMA), Multiple linear regression (MLR).

scholarly journals Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

2019 ◽  
Vol 136 ◽  
pp. 05001 ◽  
Author(s):  
Ziyuan Ye
Keyword(s):  
Air Quality ◽  
Hybrid Model ◽  
Air Pollutants ◽  
Moving Average ◽  
Mixing Time ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

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