scholarly journals Collaborative Forecasting and Analysis of Fish Catch in Hokkaido from Multiple Scales by Using Neural Network and ARIMA Model

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Yue Zhang ◽  
Masato Yamamoto ◽  
Genki Suzuki ◽  
Hiroyuki Shioya.
Keyword(s):  
Neural Network ◽  
Multiple Scales ◽  
Arima Model ◽  
Fish Catch

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

Basketball sports neural network model based on nonlinear classification

2020 ◽  
pp. 1-10
Author(s):  
Rongkai Duan ◽  
Pu Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Part ◽  
Arima Model ◽  
Research Field ◽  
Modeling And Analysis ◽  
Model Based ◽  
Bodily Injury ◽  
Nonlinear Classification

With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.

Spatiotemporal forecasting for groundwater level using a WT-LSTM model

2020 ◽  
Author(s):  
wei qin ◽  
Chengpeng Lu ◽  
Long Sun ◽  
Jiayun Lu
Keyword(s):  
Neural Network ◽  
Groundwater Level ◽  
Wavelet Transforms ◽  
Groundwater Resources ◽  
Arima Model ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Ann Model ◽  
Periodic Term ◽  
Proposed Model

<p>Accurate groundwater level forecasting models is essential to ensure the sustainable utilization and efficient protection of groundwater resources. In this paper, a novel method for groundwater level forecasting is proposed on the basis of coupling discrete wavelet transforms (WT) and long and short term memory neural network (LSTM) . In this model, the wavelet transform is used to decompose the cumulative displacement into the term of trend and term of periodicity . The trend term reflects the long-term tendency of groundwater level variation, which is simulated by a linear regression method. The periodic term driven by external factors such as rainfall, the river stage and the distance from river, is modelled using a LSTM method. The distance from river and the distance from observation wells are used for spatiotemporal model interpretation. Finally, the trend term and periodic term are superposed to achieve the cumulative spatiotemporal prediction of groundwater level. A typical study area located in Haihe basin is taken as an example to validate the performance of the proposed model. The proposed mode (WT-LSTM) is compared with the regular artificial neural network (ANN) model and autoregressive integrated moving average (ARIMA) model. The results show that the prediction accuracy of WT-LSTM model is higher than ANN model and ARIMA model, especially during the flood period. Furthermore, the spatiotemporal groundwater level forecasting is not only included the observation of groundwater and precipitation, but should also take the influence factors of surface water into consideration. The proposed model gives a new sight in the prediction of groundwater level.</p>

scholarly journals A Multiple Kernel Learning Approach for Air Quality Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hong Zheng ◽  
Haibin Li ◽  
Xingjian Lu ◽  
Tong Ruan
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Arima Model ◽  
Multiple Kernel Learning ◽  
Parametric Models ◽  
Kernel Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Multiple Kernel

Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Sign in / Sign up

Export Citation Format

Share Document