scholarly journals Coal Price Forecast Based on ARIMA Model

2021 ◽  
Vol 9 (4) ◽  
pp. 180
Author(s):  
Xiaofan Zhang ◽  
Chao Liu ◽  
Yuhang Qian
Keyword(s):  
Influencing Factors ◽  
Prediction Models ◽  
Arima Model ◽  
Policy Recommendations ◽  
Coal Price ◽  
Price Forecast ◽  
Price Prediction ◽  
Price Series ◽  
Decision Variables ◽  
Eemd Method

<div>This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing factors and the parameter estimation results. After testing, we finally choose the ARIMA 03 model to predict the next 31 days, 35 Weekly and 36-month coal prices. Finally, we combine the models and ideas of the above issues to obtain factors that affect coal price changes and related price prediction models, and combine experience to give some feasible policy recommendations.</div>

scholarly journals Next-Day Bitcoin Price Forecast

2019 ◽  
Vol 12 (2) ◽  
pp. 103 ◽  
Author(s):  
Ziaul Haque Munim ◽  
Mohammad Hassan Shakil ◽  
Ilan Alon
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Forecast Model ◽  
Test Sample ◽  
Training Sample ◽  
Forecast Performance ◽  
Autoregressive Integrated Moving Average ◽  
Price Forecast ◽  
Price Prediction ◽  
Better Than

This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

scholarly journals Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

2020 ◽  
Vol 16 (4) ◽  
pp. 584-601
Author(s):  
Chunwei Chang ◽  
Shengli Li
Keyword(s):  
Prediction Models ◽  
Interaction Effects ◽  
Sharing Economy ◽  
Tourist Attractions ◽  
Price Prediction ◽  
Price Determinants

This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants.

scholarly journals Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 4896
Author(s):  
Jianguo Zhou ◽  
Dongfeng Chen
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Emission Reduction ◽  
Search Algorithm ◽  
Carbon Price ◽  
Integration Scheme ◽  
Combination Model ◽  
Price Prediction ◽  
Price Series ◽  
Learning Machine

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

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 Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

2021 ◽  
Author(s):  
Haiying Liu ◽  
zhiqun zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Symbolic Regression ◽  
Car Ownership ◽  
Emissions Reduction ◽  
Policy Recommendations ◽  
The World ◽  
Urbanization Rate

Abstract Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emissions reduction; China is actively achieving emissions reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors, and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as type-EI, type-UR, type-GDP, type-IS, type-CO, or type-HCL regions. The decoupling index of each region showed a downward trend; EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.

scholarly journals Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

2020 ◽  
Author(s):  
Ruonan Wang ◽  
Jiancai Du ◽  
Jiangping Li ◽  
Yajuan Zhang ◽  
Jing Wen ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Regression Model ◽  
Influencing Factors ◽  
Minimum Temperature ◽  
Public Awareness ◽  
Arima Model ◽  
Northwest China ◽  
Dynamic Pattern ◽  
Prevention Campaign ◽  
Dynamic Regression Model

Abstract Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

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