Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.(c) 2023 by the authors;licensee Growing Science, Canada.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

Long-term effective and accurate wind power potential prediction, especially for wind farms, facilitates planning for the sustainable development of renewable energy. Accurate wind speed forecasting enhances wind power generation planning and reduces costs. Wind speed time series has nonlinearity, intermittence, and fluctuation, which makes the prediction difficult. Deep learning techniques can be beneficial when there is no specific structure to data. These techniques can predict wind speed with reasonable accuracy and reliability. In this study, four different algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolu-tional Neural Network (CNN), and CNN-LSTM, for three different long-term horizons (6 months, 1 year, and 5 years) are successfully developed using the direct method. GRU method showed a higher degree of accuracy compared to other methods. In addition, it is confirmed that using a multivariate data set increases the model's accuracy compared to the univariate model. A computational cost analysis is also conducted to compare the proposed algorithms. Finally, the power production capacity of the wind farm at a given location, Zabol city, is calculated for the next five years, which is indispensable for planning, management, and economic analysis. The reasonable conformance between the real data and predicted ones is shown to confirm the capability of the proposed model to use in long-term wind speed forecasting.

Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively. © 2023 by the authors;licensee Growing Science, Canada.

AI in Tourism: Leveraging Machine Learning in Predicting Tourist Arrivals in Philippines using Artificial Neural Network

Tourism is one of the most prominent and rapidly expanding sectors that contribute significantly to the growth of a country's economy. However, the tourism industry has been most adversely affected during the coronavirus pandemic. Thus, a reliable and accurate time series prediction of tourist arrivals is necessary in making decisions and strategies to develop the competitiveness and economic growth of the tourism industry. In this sense, this research aims to examine the predictive capability of artificial neural networks model, a popular machine learning technique, using the actual tourism statistics of the Philippines from 2008-2022. The model was trained using three distinct data compositions and was evaluated utilizing different time series evaluation metrics, to identify the factors affecting the model performance and determine its accuracy in predicting arrivals. The findings revealed that the ANN model is reliable in predicting tourist arrivals, with an R-squared value and MAPE of 0.926 and 13.9%, respectively. Furthermore, it was determined that adding training sets that contain the unexpected phenomenon, like COVID-19 pandemic, increased the prediction model's accuracy and learning process. As the technique proves it prediction accuracy, it would be a useful tool for the government, tourism stakeholders, and investors among others, to enhance strategic and investment decisions © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

A New Interval Type-2 Fuzzy Aggregation Approach for Combining Multiple Neural Networks in Clustering and Prediction of Time Series

Inspired by how some cognitive abilities affect the human decision-making process, the proposed approach combines neural networks with type-2 fuzzy systems. The proposal consists of combining computational models of artificial neural networks and fuzzy systems to perform clustering and prediction of time series corresponding to the population, urban population, particulate matter (PM2.5), carbon dioxide (CO2), registered cases and deaths from COVID-19 for certain countries. The objective is to associate these variables by country based on the identification of similarities in the historical information for each variable. The hybrid approach consists of computationally simulating the behavior of cognitive functions in the human brain in the decision-making process by using different types of neural models and interval type-2 fuzzy logic for combining their outputs. Simulation results show the advantages of the proposed approach, because starting from an input data set, the artificial neural networks are responsible for clustering and predicting values of multiple time series, and later a set of fuzzy inference systems perform the integration of these results, which the user can then utilize as a support tool for decision-making with uncertainty.

Evaluation of the Improvements of Transformer Prediction with Convolution

Proposed and developed 5 years ago, Transformer has been a prevailing machine learning method and is widely used to solve various kinds of practical problems [1]. According to relevant works, Transformer has performed well in both natural language processing and computer vision tasks, so we would like to test its effectiveness in prediction, specifically, time series prediction. Over the past two years, COVID-19 is no doubt one of the major factors that influences the changes in the stock prices, and the medical industry should be among the most significantly affected, which would provide an ideal sample for us to study transformer on time series prediction. In this paper, we not only construct a machine learning model using Transformer to predict the stock prices of one medical company but also add a convolution layer to try to optimize the predictions. The comparison of the outcome from the two models suggests that the convolution layer could improve the performance of the naive transformer in several ways. © 2022 IEEE.

Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

Multicommodity Prices Prediction Using Multivariate Data-Driven Modeling: Indonesia Case

One of the problems experienced by micro, small, and medium enterprises (MSMEs) during this pandemic is that most MSME actors do not understand plan-making during a crisis. This situation was exacerbated by erratic commodity prices, which resulted in several MSME players choosing to temporarily close because their turnover got a drastic decline. To help MSME actors maintain their business by knowing commodity price predictions, we propose a deep learning model using the long short-term memory (LSTM) method to predict commodity prices in Indonesia. LSTM is a type of recurrent neural network (RNN) with a memory cell to store information and solve the vanishing gradient problem in RNN. Furthermore, multivariate LSTM leverages the model to predict datasets with more than one feature. This study used a dataset collected from the Pusat Informasi Harga Pangan Strategis Nasional (PIHPS Nasional) managed by the Indonesian Ministry of Finance and Bank Indonesia consisting of significantly contributed food commodities to the formation of (strategic) inflation rates in Indonesia. The time range of commodity prices is from August 1, 2017, to July 30, 2021. There are 11 commodity price features in the dataset, namely, rice, chicken meat, eggs, onions, garlic, large red chilies, curly red chilies, red chilies, green chilies, cooking oil, and sugar. The lowest mean absolute error (MAE) on prediction is up to 255.998 obtained by the attention multivariate LSTM model with the Adam optimizer, adding batch normalization (Batchnorm) layer, reducing LSTM layer, hidden size, and grouped features. It makes the prediction more accurate and avoids overfitting and underfitting in this case. IEEE

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/data-statistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases. Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July. This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines. Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time. Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column. The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column. In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Analysis of the socioeconomic impact due to COVID-19 using a deep clustering approach.

One of the main problems that countries are currently having is being able to measure the impact of the pandemic in other areas of society (for example, economic or social). In that sense, being able to combine variables about the behavior of COVID-19 with other variables in the environment, to build models about its impact, which help the decision-making of national authorities, is a current challenge. In this sense, this work proposes an approach that allows monitoring the socioeconomic behavior of the regions/departments of a country (in this case, Colombia) due to the effect of COVID-19. To do this, an approach is proposed in which the behavior of the infected is initially predicted, and together with other context variables (climate, economics and socials) determines the current socioeconomic situation of a region. This classification of a region, with the pattern that characterizes it, is a fundamental input for those who make decisions. Thus, this work presents an approach based on machine learning techniques to identify regions with similar socioeconomic behaviors due to COVID-19, so they should eventually have similar public policies. The proposed hybrid model initially consists of a time series prediction model of infected, to which are added several context variables (climate, socioeconomic, incidence of COVID-19 at the level of deaths, suspects, etc.) in an unsupervised learning model, to determine the socioeconomic impact in the regions. Particularly, the unsupervised model groups similar regions together, and the pattern of each group describes the socioeconomic similarities between them, to help decision-makers in the process of defining policies to be implemented in the regions. The experiments showed the ability of the hybrid model to follow the evolution of the regions after 4 weeks. The quality metrics for the predictive model were around the values of 0.35 for MAPE and 0.68 for R 2 , and in the case of the clustering model were around the values of 0.3 for the Silhouette index and 0.6 for the Davies-Boulding index. The hybrid model allowed determining things like some regions that initially belonged to a group with a very low incidence of positive cases and very unfavorable socioeconomic conditions, became part of groups with moderately high incidences. Our preliminary results are very satisfactory since they allow studying the evolution of the socioeconomic impact in each region/department.

Predicting the Hate: A GSTM Model based on COVID-19 Hate Speech Datasets

COVID-19 crisis has been accompanied by copious hate speeches widespread on social media. It reinforces the fragmentation of the world, resulting in more significant racial discrimination and distrust between people, leading to crimes, and injuring individuals spiritually or physically. Hate speech is hard to crack for a global recovery in the post-epidemic era. Conducting with Twitter datasets, this paper aims to find the key indicators that influence the trend of hate speech, then builds a Gaussian Spatio-Temporal Mixture (GSTM) model for trends prediction based on the pre-analysis. Findings show that in the early period, the participation of influential users is closely related to the emergence of sentiment peaks, and the interval time is around one week. After hate speech waves up, the indicator of total exposure becomes more critical, suggesting that grass-root release influences at this stage. Compared with three classical time-series predicting models, the GSTM model shows better peak prediction ability and lower residual mean. This work enriches the approaches of predicting unknown but foreseeable hate speeches accompanied by future pandemics.

Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction.

In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.

Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks

The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic is spreading rapidly over the world and its outbreak has affected different people in different ways, it is significant to study or predict the evolution of its epidemic trend. However, most of the studies focused solely on either classical epidemiological models or machine learning models for COVID-19 pandemic forecasting, which either suffer from the limitation of the generalization ability and scalability or the lack of surveillance data. In this work, we propose T-SIRGAN that integrates the strengths of the epidemiological theories and deep learning models to be able to represent complex epidemic processes and model the non-linear relationship for more accurate prediction of the growth of COVID-19. T-SIRGAN first adopts the Susceptible-Infectious-Recovered (SIR) model to generate epidemiological-based simulation data, which are then fed into a generative adversarial network (GAN) as adversarial examples for data augmentation. Then, Transformers are used to predict the future trends of COVID-19 based on the generated synthetic data. Extensive experiments on real-world datasets demonstrate the superiority of our method. We also discuss the effectiveness of vaccine based on the difference between the predicted and the reported number of COVID-19 cases.

LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan

COVID-9 has infected nearly every country on the planet. As a result, vaccinations that can reduce our risk of contracting and spreading the COVID19 virus have been developed. As a result, each government must determine how long it will take to properly vaccinate all of its population. In this study, we built an LSTM-based prediction model to anticipate vaccination coverage in Pakistan and India. The dataset contains records of vaccine updated till January 2022. To measure the losses, we have used mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and Root mean squared error (RMSE). The model performs very well on training and testing datasets. This model can help government in the vaccination campaign. © 2022 IEEE.

COVID-19 Time Series Prediction and Lockdown Effectiveness

The origin of the COVID-19 pandemic lies at the wet market of Wuhan, China, which reportedly incepted from a person's consumption of a wild animal that was already infected with the disease. Since then, the virus has spread worldwide like wildfire and poses a major threat to the entirety of the human species itself. Coronavirus causes respiratory tract infections that can range from mild to lethal. This paper discusses the use of data analysis and machine learning to draw from the implications of the growth patterns of previous pandemics in general and projects that specifically predict future scenarios of COVID-19. It also compares and measures some of the present pandemic’s short- and long-span predictions with the equivalent real-world data observed during and after the said span. It also attempts to analyze how effective the lockdown has been across various countries and what India specifically must do to prevent a catastrophic outcome. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Data Augmentation for Short-Term Time Series Prediction with Deep Learning

In this paper, a hybrid data augmentation technique for short-term time series prediction is proposed in order to overcome the underfitting problem in deep learning models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposal hybrid technique consists of the combination of two basic data augmentation techniques that are generally used for time series classification, these are: time-warping and jittering. Time-warping allows the generation of synthetic data between each pair of values in the time series, extending its length, while jittering allows the synthetic data generated to be non-linear. To evaluate the proposal technique, it’s experimented with three non-seasonal short-term time series of Perú: CO2 emissions per capita, renewable energy consumption and Covid-19 positive cases, it is considered that predicting non-seasonal time series is more difficult than seasonal ones. The results show that the regression models based on recurrent neural networks using the selected time series with data augmentation improve results between 16.318% and 42.1426% . © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.