Prediction Of Time Series Data Research Articles

Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt

ObjectiveThis study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.MethodThe GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).ResultFor African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model’s robustness.ConclusionGRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

Time series analysis and applications in data analysis, forecasting and prediction

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.

A novel hybrid GRU-CNN and residual bias (RB) based RB-GRU-CNN models for prediction of PTB Diagnostic ECG time series data

Time series analysis is crucial in the field of medical sciences. Recently, it has become crucial to comprehend the intricacies of data to forecast and assess the likelihood of survival in patients with different ailments. The study employs diverse datasets from multiple PTB electrocardiogram (ECG) datasets, which exhibit a high degree of uncorrelation. These datasets include patients with various heart diseases and age groups, ensuring dataset diversity. The paper presents two models that utilize deep learning and residual bias. The primary approach proposed is a hybrid GRU-CNN model. In contrast, the second model incorporates residual bias (RB) to improve upon the first model, referred to as the RB-GRU-CNN model. The hybrid GRU-CNN model has been compared to conventional deep learning models across multiple metrics, achieving the highest average percentage of improvement in RMSE (46.73%), MSE (67.46%), MAPE (43.92%), and MAE (48.52%) compared to LSTM. The RB-GRU-CNN proposed model demonstrated greater efficacy than the GRU-CNN proposed model, as confirmed by non-parametric statistical Friedman ranking and their corresponding Holm’s p-values. The comprehensive study of the results proves that the proposed models outperformed the performance of the traditional models.

A novel broad learning system integrated with restricted Boltzmann machine and echo state network for time series forecasting

Time series data prediction is crucial in system control, social management, and economic production. For the complex features of time series data and the massive amount of arithmetic in deep learning, a novel network model is proposed based on the broad learning architecture to handle time series prediction tasks. The model utilizes the restricted Boltzmann machine (RBM) in the mapping layer to learn feature information from the input data. Simultaneously, it employs the echo state network (ESN) as the fundamental unit in the enhancement layer to fit the learned feature information from the mapping layer. The proposed model's predictive performance has been validated on air quality index, PM2.5, and electric power datasets. Compared with the benchmark model, it has the advantage of training speed and can reduce prediction error by up to 36%, proving its effectiveness in time series prediction tasks.

Real-time prediction of mooring tension for semi-submersible platforms

The harsh operating environment of deepwater semi-submersible production platforms poses severe challenges to the safe operation and maintenance of their production processes. Using artificial intelligence technology to improve the operation and maintenance level of large-scale offshore engineering equipment has become an inevitable trend. Accurate mooring tension prediction for semi-submersible production platforms can effectively identify potential damage and ensure the platform's safe and efficient production operation. Compared with traditional cyclic neural networks, transformer neural networks have faster calculation speeds, better generalization abilities and scalability, and better abilities to capture long-distance dependencies. In this study, the first deepwater semi-submersible production platform in the South China Sea, “DeepSea One,” was selected as the research object, and an improved novel transformer neural network architecture was proposed for the mooring tension prediction of semi-submersible production platforms using actual long-term mooring tension data obtained during the platform's production process. The proposed method was compared with the commonly used long short-term memory neural network model, and the comparison proved the effectiveness of the proposed method in predicting the dynamic tension of semi-submersible production platform mooring. In addition, the proposed method is also applicable for the prediction of other time series data in the field of shipbuilding and offshore engineering, which is of certain value for generalization.

CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.

Informer Model based Wind Power Forecast with Tropical Storms Present

When severe tropical storms pass through, regional wind speeds fluctuate greatly, and the volatility of wind farm output also increases significantly. At the same time, the duration of tropical storms is long, and it is difficult for short-term time series data prediction models to be effective, in which case the unstable output of wind turbines will have a greater impact on power system dispatching. This paper first examines the characteristics of tropical storm movement, namely the change in wind speed, and then uses the Informer long time series data prediction model to predict the total change in wind turbine output in the next 10 days after the storm has passed. The actual case proves that the Informer model is ideal in predicting the long time series of wind power output during a tropical storm.

Forecasting Methane Data Using Multivariate Long Short-Term Memory Neural Networks

AbstractOver the past few decades, Alberta has witnessed a remarkable expansion in its oil and gas sector. Unfortunately, this growth has come at a cost, as Alberta has become the fastest-growing source of pollutant emissions in greenhouse gases (GHGs), sulphur emissions, and water pollution in Canada. Among these GHGs, methane stands out as the second most prevalent GHG, possessing a global warming potential ~ 28 times higher than carbon dioxide over a span of 100 years, and ~ 80 times higher over a period of 20 years. Since 1986, the Alberta Energy Regulator (AER) has been diligently gathering data on methane concentrations. Although this data is publicly available, its analysis has not been thoroughly explored. Our study aims to investigate the impact of temperature, wind speed, and wind direction on the predictions of methane concentration time series data, utilizing a long short-term memory (LSTM) neural network model. Our findings indicate that the inclusion of climate variables enhances the predictive capabilities of the LSTM model. However, the results show that it is not obvious which variable has the most impact on the improvement although temperature appears to have a better effect on improving predictive performance compared to wind speed and direction. The results also suggest that the variance of the input data does not affect forecasting performance.

A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting

In this study, we propose a new self-organizing deep network architecture of fuzzy polynomial neural networks (FPNN) based on Fuzzy rule-based Polynomial Neurons (FPNs) and a long short-term memory (LSTM) network to solve the task of time-series forecasting. In the existing regression model based on polynomial neural networks (PNN), it is difficult to achieve high quality performance when predicting time series data, because this model lacks the ability to extract temporal and spatial information. Therefore, we propose a new architecture consisting of one LSTM (temporal) layer and several fuzzy polynomial (spatial) layers to overcome the above-mentioned shortcomings of PNN and enhance its predictive ability to approximate the data. The temporal layer consists of LSTM neurons that have inherently strong modeling capabilities to learn sequential information. The spatial layers are composed of Rule-based Polynomial Neurons (FPNs) that can effectively reflect the complex nonlinear structure found in the input space and granulate it using of the Fuzzy C-Means (FCM) clustering method. An elitism-driven roulette-wheel selection (E_RWS) is used to select appropriate neurons. E_RWS not only ensures that the neuron with the strongest fitting ability is selected but also increases the diversity of candidate neurons. According to the experimental results, the proposed model has a high prediction performance and outperforms many state-of-the-art prediction methods when applied to the real-world time-series.

Crop Monitoring System Using MODIS Time-Series Data for Within-Season Prediction of Yield and Production of US Corn and Soybeans

In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts. The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

A predictive analytics framework for sensor data using time series and deep learning techniques

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model.

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

Artificial neural networks and fuzzy structures have gained significant popularity in the last decade for time series forecasting. The objective is to conduct a performance comparison of various strategies to determine which ones are more effective for time series forecasting. The dataset provides instruction and evaluates forecasting models, utilizing artificial neural networks and fuzzy architectures. The observation evaluates the overall effectiveness of the forecasting models and the use of the root mean square error and means absolute error measures. This comparison analysis provides initial insights into the efficacy of artificial neural networks and fuzzy structures for predicting time series data. In predicting time series data, this study examines the precision of two renowned artificial intelligence systems, Neural Networks and Fuzzy structures. To evaluate the two algorithms, two distinct types of time series were utilized: a synthetic dataset consisting of 150 variables and a real-world dataset including 129 data points about monetary forecasts. The models' forecasting accuracy, training duration, and generalization abilities were compared. The findings validated that neural network surpassed fuzzy structures in all performance metrics when handling synthetic data. This research emphasizes the capabilities of artificial neural networks and fuzzy structures in addressing complicated forecasting problems. It demonstrates that both techniques may be utilized for predicting future time series values.

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

PERAMALAN HARGA BERAS PREMIUM BULANAN DI TINGKAT PENGGILINGAN MENGGUNAKAN FUZZY TIME SERIES MARKOV CHAIN

Rice is one of the crucial food commodities in Indonesia whose price fluctuates every year. Forecasting is the science of predicting an event in the future and predicting future conditions using historical data. One of the forecasting methods is the Fuzzy Time Series which is used to predict time series data that can be widely used on any real time data. This research used forecasting with the Fuzzy Time Series Markov Chain method because this method provides a good accuracy value. The historical data used is monthly data on the average price of premium rice at the Indonesian mill level for the period January 2014-July 2022 then divided into training data and testing data. The error rate used is MAPE and the results of calculations with Fuzzy Time Series Markov Chain on data testing the period November 2020-July 2022 obtained a very good MAPE value of 0.81%. Forecasting results for the period August 2022 obtained the results of Rp. 9.627,99

Bayesian Structural Time Series Model for Forecasting the Composite Stock Price Index in Indonesia

One of the models that can be used to predict time series data is the Bayesian Structural Time Series (BSTS) model. The BSTS model is a more modern model and can handle data movement better. In the BSTS model, the Markov Chain Monte Carlo (MCMC) sampling algorithm is used to simulate the posterior distribution, which smoothes the forecasting results over a large number of potential models using Bayesian averaging models. The purpose of this study was to obtain the best BSTS model for Composite Stock Price Index (CSPI) data in Indonesia based on the state component and the number of MCMC iterations, and obtain forecasting results for CSPI value in Indonesia for the next 24 months, namely the period July 2023 to June 2024. The results obtained are based on a comparison of the R-square values in the model, the BSTS model with local linear trend and seasonal state components, and the number of MCMC iterations n = 5 00 is the best BSTS model that can be used for forecasting the CSPI value in Indonesia with an R-square value of 99.96%. The results of forecasting the CSPI value in Indonesia for the period July 2023 to June 2024 range from 6589 to 6760, with the lowest forecasting value in October 2023 and the highest in March 2023.

신재생 에너지 최적 활용을 위한 축열조 온도 예측 모델 연구

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

Economy and carbon emissions optimization of different provinces or regions in China using an improved temporal attention mechanism based on gate recurrent unit

With the implementation of 14th Five-Year Plan in China and the completion of the poverty alleviation task, the economy in China has made great progress. However, the carbon dioxide (CO2) emission has also increased, which will lead to the greenhouse effect for threatening economic development and affecting people's lives. Global warming has become the greatest challenge to contemporary economic and social development, and the transition to low-carbon economy has become a general trend of world economic development. Meanwhile, the traditional neural network method for predicting the economy and CO2 emissions is not accurate and objective because of the unbalanced labeling of training data and the inability to capture the relationship between time series data. Therefore, an economy and CO2 emission prediction model based on temporal attention Gate Recurrent Unit (GRU) (TA-GRU) with loss function Balanced Mean Square Error (BMSE) (BTA-GRU) mechanism is proposed to analyze and optimize the energy structure of 27 provincial-level administrative regions in China. Due to the unbalanced data labels in the different energy structure of different regions, the improved loss function BMSE is used to solve the problem of unbalanced labels from the perspective of statistics. Then, with coal, gasoline, petroleum, coke, fuel oil, diesel, natural gas and crude oil as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, the GRU can better capture time step dependencies in time series, and the attention mechanism can assign different weights to each time step, so that the proposed method can better learn data features and improve the accuracy of the an economic and CO2 emission prediction model. Finally, the CO2 emission and economic prediction model of different provincial-level administrative regions in China is established based on this proposed method. The experimental results show that compared with other mainstream time series data prediction models, this proposed model has better stability and practicability, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) reach 3.43, 3.28% and 2449.57, respectively. Furthermore, according to the actual situation of efficient provincial administrative regions, the energy structure of inefficient provincial administrative regions can be adjusted and optimized. Such as the energy structure of Hebei Province can be adjusted according to the energy structure of Beijing, it is estimated that the CO2 emissions of Hebei will be reduced by 82,972.43 ×10000 tons (WT) and its per capita GDP will be increased by 115,856 Yuan. Moreover, Chinese provincial governments should vigorously promote the use of clean energy, accelerate the establishment of a sound green low-carbon circular development economic system, and help achieve the goal of carbon neutrality.