scholarly journals Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Bilin Shao ◽  
Maolin Li ◽  
Yu Zhao ◽  
Genqing Bian
Keyword(s):  
Prediction Accuracy ◽  
Short Term Memory ◽  
High Reliability ◽  
Moving Average ◽  
Arima Model ◽  
Pso Algorithm ◽  
Metal Exchange ◽  
Nickel Metal ◽  
Learning Factor ◽  
Improved Pso

Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.

scholarly journals Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China

2021 ◽  
Vol 18 (11) ◽  
pp. 6174
Author(s):  
Rui Zhang ◽  
Zhen Guo ◽  
Yujie Meng ◽  
Songwang Wang ◽  
Shaoqiong Li ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Warning System ◽  
Meteorological Variables ◽  
Training Set ◽  
Interference Factor ◽  
Test Set ◽  
Autoregressive Integrated Moving Average

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

scholarly journals Comparison of ARIMA, ANN and LSTM for Stock Price Prediction

2020 ◽  
Vol 218 ◽  
pp. 01026
Author(s):  
Qihang Ma
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Ability ◽  
Research Direction ◽  
Ann Model ◽  
Advantages And Disadvantages ◽  
Stock Price Prediction

The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.

scholarly journals Well-Logging Prediction Based on Hybrid Neural Network Model

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8583
Author(s):  
Lei Wu ◽  
Zhenzhen Dong ◽  
Weirong Li ◽  
Cheng Jing ◽  
Bochao Qu
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Evaluation Method ◽  
Oil And Gas ◽  
Short Term Memory ◽  
Pso Algorithm ◽  
Well Logging ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.

scholarly journals Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1235
Author(s):  
Mirche Arsov ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
Roberto Corizzo ◽  
Nikola Koteli ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Measurement Data ◽  
Quality Measurement ◽  
Capital City ◽  
Industrial Plants ◽  
Proactive Measures

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

scholarly journals Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 409
Author(s):  
Samer Chaaraoui ◽  
Matthias Bebber ◽  
Stefanie Meilinger ◽  
Silvan Rummeny ◽  
Thorsten Schneiders ◽  
...  
Keyword(s):  
Linear Regression ◽  
Health Facility ◽  
Statistical Approach ◽  
Short Term Memory ◽  
High Reliability ◽  
Moving Average ◽  
Health Sector ◽  
Cost Effective ◽  
System Control ◽  
Electrical Load

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.

scholarly journals PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS

2019 ◽  
Vol 20 (5) ◽  
pp. 920-938 ◽  
Author(s):  
Ayşe Soy Temür ◽  
Melek Akgün ◽  
Günay Temür
Keyword(s):  
Real Estate ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Supply And Demand ◽  
Arima Model ◽  
Housing Prices ◽  
Percentage Error ◽  
Data Set ◽  
Technological Advances

Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values ​​obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.

scholarly journals A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tailai Wen ◽  
Gang Ou ◽  
Xiaomei Tang ◽  
Pengyu Zhang ◽  
Pengcheng Wang
Keyword(s):  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Communication Link ◽  
Satellite Clock ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Galileo Satellite ◽  
Clock Offset

The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction.

scholarly journals Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 10 ◽  
Author(s):  
Minglu Ma ◽  
Zhuangzhuang Wang
Keyword(s):  
South Africa ◽  
Energy Consumption ◽  
High Reliability ◽  
Moving Average ◽  
Supply And Demand ◽  
Arima Model ◽  
Research Result ◽  
Grey Model ◽  
African Countries ◽  
Autoregressive Integrated Moving Average

South Africa’s energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of South Africa’s energy consumption during 1998–2016, Autoregressive Integrated Moving Average (ARIMA) model, nonlinear grey model (NGM) and nonlinear grey model–autoregressive integrated moving average (NGM-ARIMA) model are adopted to predict South Africa’s energy consumption during 2017–2030. After using these NGM, ARIMA and NGM-ARIMA, the mean absolute percent errors (MAPE) are 2.827%, 2.655% and 1.772%, respectively, which indicates that the predicted result has very high reliability. The prediction results show that the energy consumption in South Africa will keep increasing with the growth rate of about 7.49% in the next 14 years. This research result will provide scientific basis for the policy adjustment of energy supply and demand in South Africa and the prediction techniques used in the research will have reference function for the energy consumption study in other African countries.

scholarly journals COMPARING THE PREDICTION ACCURACY OF LSTM AND ARIMA MODELS FOR TIME-SERIES WITH PERMANENT FLUCTUATION

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Ghahreman Abdoli ◽  
Mohsen MehrAra ◽  
Mohammad Ebrahim Ardalani
Keyword(s):  
Time Series ◽  
Economic System ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Economic Time Series ◽  
Intraday Data

In developing countries with an unstable economic system, permanent fluctuation in historical data is always a concern. Recognizing dependency and independency of variables are vague and proceeding a reliable forecast model is more complex than other countries. Although linearization of nonlinear multivariate economic time-series to predict, may give a result, the nature of data which shows irregularities in the economic system, should be ignored. New approaches of artificial neural network (ANN) help to make a prediction model with keeping data attributes. In this paper, we used the Tehran Stock Exchange (TSE) intraday data in 10 years to forecast the next 2 months. Long Short-Term Memory (LSTM) from ANN chooses and outputs compared with the autoregressive integrated moving average (ARIMA) model. The results show, although, in long term prediction, the forecast accuracy of both models reduce, LSTM outperforms ARIMA, in terms of error of accuracy, significantly.

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