Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries

In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The database employed for this research contained the IBEX35 index values and stock closing prices from 3 January 2000 to 30 December 2022. The models trained with this data were employed in order to forecast the index value and the closing prices of the stocks under study from 2 January 2023 to 24 March 2023. The results obtained confirmed that although none of the proposed models outperformed the rest for all the companies, it is possible to calculate forecasting models able to predict a 95% confidence interval about real stock closing values and where the index will be in the following three months.

Comparison of Active COVID-19 Cases per Population Using Time-Series Models

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the 36 different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered, and death cases as of 4 November 2020. A 14step forecast system for active coronavirus cases was built, analyzed, and compared for six different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states, respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.Alternate :Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados.Alternate :抄録本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

Strategy for Services Anticipation at the Rehabilitation Center of BNN Based on Moving Average and Exponential Smoothing Forecasting Method

One of the Indonesian government's responses to the COVID-19 pandemic is making policies related to restrictions on public services which affects the organizational resilience of the Rehabilitation Center of the National Narcotics Board (BNN). This research aimed to determine the historical pattern of the influence of public service policies during the COVID-19 pandemic on the client population, to forecast the client population for 3 (three) months ahead, and to analyze strategies for anticipating rehabilitation services at the Rehabilitation Center of BNN. This research method is quantitative by using a moving average (MA) and exponential smoothing forecasting model. Based on the validity test, MA is the best forecasting model, which indicates a possibility of a spike in male clients with the same amount in the pre-pandemic period, as many as 310 people, and the average female client is 7 people. Meanwhile, adolescent clients show inaccurate prediction results with MAPE 149.825. Strategies that can be implemented to anticipate a spike in the number of clients if it reaches the highest forecasting point are: increasing the budget, modifying the rehabilitation program for female and adolescent clients, a balanced staff composition, and the availability of facilities and infrastructure. [ FROM AUTHOR] Copyright of Technium Social Sciences Journal is the property of Technium Press Constanta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Omicron Case Prediction using Machine Learning

It's been over two years that the world has been dealing with the novel Coronavirus Disease 2019 (COVID-19). It has rocked the world in the face of another major outbreak. Countries have undergone various lockdowns curfews in their own ways, which certainly has impacted our daily lives. COVID-19 has undergone various mutations till now. It is responsible for the spikes in COVID-19 cases across the world. The latest variant 'Omicron'., labeled as B.1.1.529, has been marked as a Variant of Concern by the World Health Organization (WHO). It has been proven to be the most infectious, but less deadly as of now. This paper attempts to propose an analysis and prediction of Omicron daily cases in India using SARIMA Exponential Smoothing Machine Learning models. Both of these machine learning models are based on the time series forecasting concept and rely on previous data to predict future outcomes. © 2022 IEEE.

Forecasting Offline Retail Sales in the COVID-19 Pandemic Period: A Case Study of a Complex Shopping Mall in South Korea

This study examines the case of a shopping mall in Seoul, South Korea, based on its offline retail sales data during the period of the enforcement of the COVID-19 pandemic social distancing policy. South Korea implemented strict social distancing, especially in retail categories where people eat out, due to the danger of spreading infectious disease. A total of 55 retail shops' sales data were analyzed and classified into five categories: fashion, food and beverage (f&b), entertainment, cosmetics and sport. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were employed, and the autocorrelation (ACF) and partial autocorrelation (PACF) of each retail category's sales data were analyzed. The mean absolute percentage error (MAPE) was used to determine the most suitable forecasting model for each retail category. In this way, the f&b and entertainment retail categories, in which people eat out, were found to have been significantly impacted, with their 2022 sales forecasted to be less than 80% of their 2018 and 2019 sales. The fashion retail category was also significantly impacted, slowly recovering sales in 2022. The cosmetics and sport retail categories were little impacted by the COVID-19 outbreak, with their retail sales having already recovered by 2022. © 2023 by the authors.

Forecasting the number of road accidents in Poland by day of the week and the impact of pandemics and pandemic-induced changes

The COVID19 pandemic has significantly affected the performance of the transport sector and its overall intensity. Reduced mobility has a large impact on the number of road accidents. The aim of this study is to forecast the number of road accidents in Poland and to assess the impact of the COVID19 pandemic on the variation in road crashes. For this purpose, day-wise historical crash data from 2011 onwards have been collected and analysed. Based on real historical field data, the future has been forecasted for both pandemic and nonpandemic variants. Forecasting of the number of accidents has been carried out using selected time series models and exponential models. Based on obtained data, it can be stated that pandemic resulted in a decrease in number of road accidents in Poland with ranges of reduction varying from 11% to 30% based on different days of week. Most visible decrease is observed on 3 days viz. Monday, Wednesday, and Saturday. Further, the projections show that in view of the current situation one may expect further decrease in the number of road accidents in Poland. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Forecasting Combination of Hierarchical Time Series: A Novel Method with an Application to CoVid-19

Multiple, hierarchically organized time series are routinely submitted to the forecaster upon request to provide estimates of their future values, regardless the level occupied in the hierarchy. In this paper, a novel method for the prediction of hierarchically structured time series will be presented. The idea is to enhance the quality of the predictions obtained using a technique of the type forecast reconciliation, by applying this procedure to a set of optimally combined predictions, generated by different statistical models. The goodness of the proposed method will be evaluated using the official time series related to the number of people tested positive to the SARS-CoV-2 in each of the Italian regions, between February 24th 2020 and August 31th 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Future Forecasting Using Machine Learning Models

Covid-19 is a very infectious virus. According to World Health Organization (WHO), millions of individuals have been diagnosed with Covid-19 since then, and at least a million have died as the virus has expanded dramatically. While most of the news on this front is scary, technology is helping to pave the path through this crisis. Manual forecasting is a difficult challenge for humans due to its large scale and complexity. Machine Learning (ML) techniques can effectively predict Covid-19 infected patients. There are a lot of study that have been developed to predict and forecast the future number of cases affected by Covid-19. In this area, our forecasting can be tackled as a problem of supervised learning. Supervised ML is very popular regression methods due to its simplicity to be interpreted by Humans. In this paper, we use two datasets to predict the symptoms through two different types of regression algorithms (single and multiple regression), the ML algorithms are LR, SVM, LASSO, ES and Polynomial regression, for the multiple regression we used LR, SVM and LASSO. The obtained results validate that for the single regression the Exponential Smoothing (ES) outperforms other machine learning approaches like Linear Regression (LR) and LASSO in terms of R-Square, Adjusted R-Square, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The same accuracy is observed for the models used in the multiple regression. © 2022 IEEE.

Predictive Analytics Comparison of Indonesia Government Budget With or Without Covid-19 Pandemic

An accurate budget forecast of Indonesia before starting a financial year is very important, the budget can be used to direct socio-economic development, ensure sustainability, and improve the quality of life of the people. Since the pandemic covid occurred in 2020 many countries carry out social restrictions to regional or national lockdown that impact the national budget. To estimate the Indonesian government's budget and accurately at the planning stage with the assumption the pandemic will continue or end. The solution can be figured out by using predictive analytics. Predictive analytics seeks to predict the future by examining historical data, detecting patterns or relationships in these data, and connecting all historical data patterns in the future. This solution can be used to predict how much the Indonesia Budget will be with the assumption previously mention. © 2022 IEEE.

Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Türkiye

In the present paper, a forecasting study on the monthly electricity generation of Türkiye from the conventional and renewable resources is performed. The effect of the CoVid-19 pandemic on the sector has been considered. For this aim, the trend before the pandemic has been initially considered and later the post-pandemic situation has been handled. It has been observed that the electricity generation supply/demand mechanism changes drastically compared to the pre- and post-pandemic cases. The rate of the generation from the renewable resources especially shows a sharp variation compared to the rates from the fossil fuels. According to the forecasting scenario, in 2021, the electricity generation shows different attitudes with regard to the resources used. In 2022, especially increasing trends are expected for wind, biogas, natural gas, imported coal and fuel oil, whereas diesel and mineral coal are expected to be decreased in Türkiye. © 2022 Published by peer-reviewed open access scientific journal, JES at DergiPark.

Forecasting of COVID-19 Cases in India Using Machine Learning: A Critical Analysis

COVID-19 is an infectious disease that has spread over the world since the first case was discovered in China in December 2019. Multiple variants of COVID-19 have been discovered in the last two years, indicating that it is highly mutable. The most recent variant is omicron, which has similar transmissibility to the delta variant and so has a significant risk of producing a third wave in India. This study analyzes five distinct time series forecasting models: autoregression (AR), exponential smoothing (ES), multilayer perceptron (MLP), long-short term memory (LSTM), autoregressive integrated moving average (ARIMA), and their hybrid models. The purpose of this research is to find the best machine learning model for forecasting COVID-19 cases, as the number of novel variant omicron cases in India is on the rise. As a result, it is necessary to forecast COVID-19 cases to make appropriate precautionary actions in order to avert the third wave of COVID-19 in India. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The effectiveness analysis of selected IT tools for predictions of the Covid-19 pandemic

The article presents the problem of the complexity of prediction and the analysis of the effectiveness of selected IT tools in the example of the Covid-19 pandemic data in Poland. The study used a variety of tools and methods to obtain predictions of extinct infections and mortality for each wave of the Covid-19 pandemic. The results are presented for the 4th wave with a detailed description of selected models and methods implemented in the prognostic package of the statistical programming language R, as well as in the Statistica and Microsoft Excel programs. Naive methods, regression models, exponential smoothing methods (including ETS models), ARIMA models, and the method of artificial intelligence - autoregressive models built by neural networks (NNAR) were used. Detailed analysis was performed and the results for each of these methods were compared. © 2022 Polish Information Processing Society.

Impact of the COVID-19 pandemic on road traffic accident forecasting in Poland and Slovakia

The COVID-19 pandemic significantly affected the performance of the transport sector and its overall intensity. Reducing mobility has a major impact on road traffic accidents. The aim of this study is to forecast the number of road traffic accidents in Poland and Slovakia and to assess how the COVID-19 pandemic affected its trend. For this purpose, data for Poland and Slovakia in the selected relevant period were analyzed. Based on actual data from the past, a forecast was made for the future considering two scenarios - one where there is no effect of pandemic, and another with effect of pandemic. Forecasting the number of accidents in Poland was carried out using selected time series models related to linear trend (Holt and Winters method) and the exponential model. In the case of Slovakia, the model without trend and the exponential model were used to forecast the number of traffic accidents. The results of the research show that the pandemic caused a decrease in the number of traffic accidents in Poland by 31% and in Slovakia by 33%. This is a significant decline, but it is linearly dependent on restrictive measures that affect the mobility of the population. A similar trend can therefore be expected on a European scale.

Performance Analysis of the Triple Exponential Smoothing Method during the Covid19 Pandemic on Tourist Visit Data

Data forecasting methods are essential in the business world to determine the company's future steps. However, the COVID-19 pandemic has hit the tourism economy hard, resulting in a slump in income. In this study, trials were conducted to analyze the reliability of forecasting methods on data affected by the COVID-19 pandemic. The method used is the Triple Exponential Smoothing method involving two models, namely Additive and Multiplicative. In this paper, the test is carried out using actual data derived from data from a service company engaged in tourist crossing transportation. Each method's alpha, beta, and gamma values are determined based on the parameters that produce the smallest error value. The experiment results show the predictability of the Triple Exponential Smoothing method by measuring the prediction error value based on the Mean Absolute Percentage Error (MAPE) value, which was 7.56% in the Additive model and 10.32% in the Multiplicative model before the pandemic happened. However, both methods' prediction measurements during a pandemic produce poor forecasts with an error percentage above 40%. Meanwhile, during the decline in pandemic cases, the value of the Triple Exponential Smoothing Multiplicative method was closer to the actual data with a prediction error value of 33.02%. Therefore, the Triple Exponential Smoothing Multiplicative method is more resistant and suitable for implementing into a forecasting system with actual data that influences pandemic events. © 2022 IEEE.

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Human action analysis is an enthralling area of research in artificial intelligence, as it may be used to improve a range of applications, including sports coaching, rehabilitation, and monitoring. By forecasting the body's vital position of posture, human action analysis may be performed. Human body tracking and action recognition are the two primary components of video-based human action analysis. We present an efficient human tracking model for squat exercises using the open-source MediaPipe technology. The human posture detection model is used to detect and track the vital body joints within the human topology. A series of critical body joint motions are being observed and analysed for aberrant body movement patterns while conducting squat workouts. The model is validated using a squat dataset collected from ten healthy people of varying genders and physiques. The incoming data from the model is filtered using the double exponential smoothing method;the Mean Squared Error between the measured and smoothed angles is determined to classify the movement as normal or abnormal. Level smoothing and trend control have parameters of 0.8928 and 0.77256, respectively. Six out of ten subjects in the trial were precisely predicted by the model. The mean square error of the signals obtained under normal and abnormal squat settings is 56.3197 and 29.7857, respectively. Thus, by utilising a simple threshold method, the low-cost camera-based squat movement condition detection model was able to detect the abnormality of the workout movement.

Forecasting of COVID-19 incidence in Ukraine using the method of exponential smoothing.

Coronavirus infection (COVID-19) is a highly infectious disease of viral etiology. SARS-CoV-2 virus was first identified during the investigation of the outbreak of respiratory disease in Wuhan, China in December 2019. And already on March 11, 2020 COVID-19 in the world was characterized by the WHO as a pandemic. In Ukraine the situation with incidence COVID-19 remains difficult. The purpose of this study is to to develop a mathematical forecasting model for COVID-19 incidence in Ukraine using an exponential smoothing method. The article analyzes reports on basic COVID-19 incidence rates from 29.02.2019 to 01.10.2021. In order to determine the forecast levels of statistical indicators that characterize the epidemic process of COVID-19 the method of exponential smoothing was used. It is expected that from 29.02.2019 to 01.10.2021 the epidemic situation of COVID-19 incidence will stabilize. The indicator of "active patients" will range from 159.04 to 353.63 per 100 thousand people. The indicator of "hospitalized patients" can reach 15.43 and "fatalities" ‒ 1.87. The use of the method of exponential smoothing based on time series models for modeling the dynamics of COVID-19 incidence allows to develop and implement scientifically sound methods in order to prevent, quickly prepare health care institutions for hospitalization.

Time Series Analysis of COVID-19 Data-A study from Northern India

The continuing new Coronavirus (COVID-19) pandemic has caused millions of infections and thousands of fatalities globally. Identification of potential infection cases and the rate of virus propagation is crucial for early healthcare service planning to prevent fatalities. The research community is faced with the analytical and difficult real-world task of accurately predicting the spread of COVID-19. We obtained COVID-19 temporal data from District Surveillance Officer IDSP, Dehradun cum District Nodal Officer-Covid-19 under CMO, Department of Medical Health and Family Welfare, Government of Uttarakhand State, India, for the period, March 17, 2020, to May 6, 2022, and applied single exponential method forecasting model to estimate the COVID-19 outbreak's future course. The root relative squared error, root mean square error, mean absolute percentage error, and mean absolute error were used to assess the model's effectiveness. According to our prediction, 5438 people are subjected to hospitalization by September 2022, assuming that COVID cases will increase in the future and take on a lethal variety, as was the case with the second wave. The outcomes of the forecasting can be utilized by the government to devise strategies to stop the virus's spread. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.

Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study.

Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.

Future Prediction of COVID-19 based on Supervised Machine Learning Models

In the medical history, Covid-19 case was first reported in the Chinese city of Wuhan, and as the cases spread rapidly, Covid-19 cases are reported all over the world, causing extensive loss of humanity. Additionally, the Covid-19 virus might be symptomatic or asymptomatic, this complicates the process of identifying the individual, who has been infected with Covid-19. People infected with this virus may experience symptoms like fever, headaches, respiratory issues, and, in exceptional situations, death. The primary objective of this research work is to forecast the number of cases in the future so that the government and the public can adopt the required preventive and safety measures. © 2022 IEEE.