Demand Forecasting in SCM for Automobile Industry based on Time-series Modeling

In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.

A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19.

There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA DCV model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA BCV). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA DCV performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.

Increase in traumatic injury burden amidst COVID-19 was disproportionately shouldered by racial and ethnic minority patients: An urban case study

Background: When the COVID-19 pandemic intersected with the longstanding global pandemic of traumatic injury, it exacerbated racial and ethnic disparities in injury burden. As Milwaukee, Wisconsin is a racially diverse yet segregated urban city due to historic and ongoing systemic efforts, this populace provided an opportunity to further characterize injury disparities. Method(s): We analyzed trauma registry data from the only adult Level 1 trauma center in Milwaukee, WI before and during the COVID-19 pandemic (N = 19,908 patients from 2015-2021). We retrospectively fit seasonal ARIMA models to monthly injury counts to determine baseline injury burden pre-COVID-19 (Jan 2015-Mar 2020). This baseline data was used to forecast injury by race and ethnicity from April 2020 to December 2021 and was compared to actual injury counts. Result(s): For all mechanisms of injury (MOI), counts during the pandemic were significantly higher than forecasted for Black or African American (mean absolute percentage error, MAPE = 23.17) and Hispanic or Latino (MAPE = 26.67) but not White patients (MAPE = 12.72). Increased injury for Black or African American patients was driven by increases in motor vehicle crashes (MVCs) and firearm-related injury;increased injury for Hispanic or Latino patients was driven by falls and MVCs. Conclusion(s): The exacerbation of injury burden disparities during COVID-19, particularly in specific MOI, underscores the need for primary injury prevention within specific overburdened communities. Injury prevention requires intervention through social determinants of health, including addressing the impact of structural racism, as primary drivers of injury burden disparities.Copyright © The Author(s) 2023.

The long-run analysis of COVID-19 dynamic using random evolution, peak detection and time series.

It is now almost three years that COVID-19 has been the cause of misery for millions of people around the world. Many countries are in process of vaccination. Due to the social complexity of the problem, the future of decisions is not clear. As such, there is a need for the mathematical modeling to predict the long-run behavior of the COVID-19 dynamic for the decision-making with regard to the result of the pandemic on the economy, health, and others. In this paper, we have studied the short and long-run behavior of COVID-19. In a novel way, random evolution (Trichotomous and Dichotomous Markov Noise) is used to model and analyze the long-run behavior of the pandemic in different phases of the pandemic in different countries. On the given conditions, the random evolution model can help us establish the long-run asymptotic behaviour of the pandemic. This allows us to consider different phases of the pandemic as well as the effect of vaccination and other measures taken. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. As such, we have established a criterion for the comparison of different regions and countries in different phases. In this regard, we have used real pandemic data from different countries to validate our results.

Analysis of the unemployment gender gap in Colombia 2001-2021: A SARIMA-GARCH model approach

Reducing the unemployment gender gap is seen as an indicator of women's empowerment capacity for the equitable growth of the country's economy. At the regional level, Colombia exhibits one of the highest unemployment gaps, despite the efforts made to close them. The objective of this study is to model the evolution of the unemployment gender gap in Colombia during the period 2001:01 to 2021:06, to forecast its behavior, and determine its volatility. For this purpose, a Seasonal Autoregressive Integrated Moving Averages (SARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity model (GARCH) were fitted. The results indicate that, although the gender gap had been slightly declining in the last two decades, it was adversely affected by the Covid-19 pandemic, causing the gap to increase again. On the other hand, there is an increase in the volatility of the series, making it more vulnerable to economic and seasonal cycles. Finally, it is forecast that the gap will tend to decrease in the following months, however, it will increase again in December due to the seasonal component.

Using the Statistical Machine Learning Models ARIMA and SARIMA to Measure the Impact of Covid-19 on Official Provincial Sales of Cigarettes in Spain

From a public health perspective, tobacco use is addictive by nature and triggers several cancers, cardiovascular and respiratory diseases, reproductive disorders, and many other adverse health effects leading to many deaths. In this context, the need to eradicate tobacco-related health problems and the increasingly complex environments of tobacco research require sophisticated analytical methods to handle large amounts of data and perform highly specialized tasks. In this study, time series models are used: autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to forecast the impact of COVID-19 on sales of cigarette in Spanish provinces. To find the optimal solution, initial combinations of model parameters automatically selected the ARIMA model, followed by finding the optimized model parameters based on the best fit between the predictions and the test data. The analytical tools Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. The evaluation metrics that are used as criteria to select the best model are: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean error (ME) and mean absolute standardized error (MASE). The results show that the national average impact is slight. However, in border provinces with France or with a high influx of tourists, a strong impact of COVID-19 on tobacco sales has been observed. In addition, the least impact has been observed in border provinces with Gibraltar. Policymakers need to make the right decisions about the tobacco price differentials that are observed between neighboring European countries when there is constant and abundant cross-border human transit. To keep smoking under control, all countries must make harmonized decisions. © 2023, Universidad Internacional de la Rioja. All rights reserved.

Covid Wave Prediction using SARIMA Machine Learning Algorithm

A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs. © 2023 IEEE.

Machine Learning Based Predictive Analytics for Agriculture Inventory Management System

When the globe was hit by the vicious Covid 19 pandemic, multiple industries faced the virus's wrath and that included the agricultural warehouse industry. Consequently, many warehouses which had received large shipment stocks of agricultural products were never to be used again as it had reached its expiration date. This led to major losses for the agricultural warehouses as well as losses in crops for farmers and large scale agriculturists. The main objective of this paper is to build a model which utilises 3 heavy-weight algorithms (Seasonal Autoregressive Integrated Moving Average-SARIMA, Long short term memory-LSTM and Holt Winters) and predicts the agricultural needs of retailers and consumers based on previous data from different warehouses. Deploying this system will not help in the regulation of goods in warehouses but will also aid in maximizing the profits and minimizing the losses for warehouses. The algorithm with the least MAE(Mean Absolute Error) value will be considered for forecasting the sales of the aforementioned product. © 2022 IEEE.

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.

Research and Design of an Emergency Supply Assurance Monitoring System in the Post-epidemic Context

At present, the COVID-19 epidemic is still ravaging the world, and the domestic epidemic is still recurring and continues to affect people's life and work. The research and design of an emergency supply assurance monitoring system in response to the epidemic and other emergencies, which provides the competent authorities with monitoring alert and trend data of supply, demand and price of essential goods market, is of great significance to stabilize people's basic essential goods materials. Based on the data of essential goods under epidemic, the system carries out the construction and application of monitoring and warning model and RNN-SARIMA hybrid model. Through the research and design of the system, monitoring and warning of abnormal fluctuations of essential goods and predicting price trends are realized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fertility response to the covid-19 pandemic in developed countries - on pre-pandemic fertility forecasts

The COVID-19 pandemic has affected all areas of our lives. Among other outcomes, the academic literature and popular media both discuss the potential effects of the pandemic on fertility. As fertility is an important determinant of population development and population forecasts are important for policy decisions and planning, we need to address to which extent fertility forecasts performed before the pandemic still apply. Using Monte Carlo forecasting based on principal components of fertility rates, we quantify the effects of the pandemic on fertility for 22 countries and discuss whether forecasts made prior to the pandemic need adjustment based on more recent data. Among the studied countries, 14 countries show no signifi cant effect of the pandemic at all, while six countries have signifi cantly lowered numbers of births in comparison to counterfactual trajectories that assume that past trends will hold. These countries are primarily in the Mediterranean and East Asia. For Finland and South Korea, there is statistical evidence for increased fertility in the early phases of the pandemic. In all cases with statistically signifi cant fertility differentials caused by the pandemic, reproductive behavior normalized quickly. Therefore, we fi nd no evidence for long-term effects of the pandemic on fertility, leading to the conclusion that pre-pandemic fertility forecasts still apply. © 2023, Bundesinstitut fur Bevolkerungsforschung. All rights reserved.

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE.

Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019-2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from "very good" (MAPE of 8.64% at the Liwa station with the closed loop) to "acceptable" (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

COVID-19 Impact on Ethanol Sales in Fuel Stations: An ITS Econometric Analysis

This paper provides estimates of the impact of the COVID-19 outbreak on Brazilian Ethanol sales. To this end, weekly data on Ethanol sales volumes are analyzed through an ITS SARIMA model and a counterfactual analysis covering the 2019–2020. We find that the real effect of COVID-19 was a reduction above 77.97% in Brazil after the first COVID-19 death, in March 2020, and still a decrease of about 50.15% at the end of 2020. The empirical evidence confirms that the impact of the pandemic crisis, the counterfactual analysis allows estimating the real effect of COVID-19 is on average 3.76% greater than the observed against an index date reference. These results suggest that ethanol sales in Brazil were more affected than only when comparing previous results to the effects of the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.

Objective: Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China. Methods: Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods. Results: From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (ß2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447). Conclusions: The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

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.

Statistical Analysis of Covid-19 Pandemic in Saudi Arabia

COVID-19, a new coronavirus illness, initially reported in China in December 2019 has spread around the world. COVID-19 coronavirus has evolved into a worldwide health hazard, quickly infecting humans. Controlling the outbreak is crucial, and scientists have continued to look at potential treatments. COVID-19 can also be defeated with supportive treatment and hospital critical care services. COVID-19 might be avoided using statistical forecasting techniques. The purpose of this study is to create a forecasting model that could be used to predict the spread of COVID-19 in Saudi Arabia. An autoregressive (AR) integrated moving average (ARIMA) model was used to anticipate the number of deaths in three key Saudi Arabian regions: Riyadh, Eastern Region, and Qassim. According to our findings, the number of fatalities in Riyadh and Eastern Region was expected to decrease in August (2021), while the deaths in Qassim were expected to decrease in July (2021).

Forecasting pharmacy purchases orders

Inventory represents the largest asset in pharmacy products distribution. Forecasting pharmacy purchases is essential to keep an effective stock balancing supply and demand besides minimizing costs. In this work, we investigate how to forecast product purchases for a pharmaceutical distributor. The data contains inventory sale histories for more than 10 thousand active products during the past 15 years. We discuss challenges on data preprocessing of the pharmacy data including cleaning, feature constructions and selections, as well as processing data during the COVID period. We experimented on different machine learning and deep learning neural network models to predict future purchases for each product, including classical Seasonal Autoregressive Integrated Moving Average (SARIMA), Prophet from Facebook, linear regression, Random Forest, XGBoost and Long Short-Term Memory (LSTM). We demonstrate that a carefully designed SARIMA model outperformed the others on the task, and weekly prediction models perform better than daily predictions.

Health condition prediction and covid risk detection using healthcare 4.0 techniques

Healthcare 4.0 is one of the emerging concepts that has grabbed the interest among researchers as well as the medical sector. Using the Internet of Things (IoT) and sophisticated communication technologies, it is now possible to monitor the patient from a remote area. In this paper, we design a remote health monitoring system using IoT and Machine Learning (ML) to determine the health condition of a patient. Supervised ML algorithms along with a time-series model such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model are applied on the gathered data from IoT medical sensors to predict the health status of a patient. We consider a use-case of covid and compared it with our sensor data by applying the unsupervised ML algorithm, Long Short Term Memory (LSTM) along with a stochastic model, namely Markov Model to detect the risk of getting covid for a particular patient. LSTM with Markov model provides better results for detection with root mean squared error (RMSE) of 0.18 as against the RMSE of 0.45 obtained with only LSTM. We further design an optimization algorithm using “fuzzy logic” that attains optimum results in detecting the risk of getting covid.

Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?

Foreign Exchange (FOREX) is a decentralized global market for exchanging currencies. The Forex market is enormous, and it operates 24 h a day. Along with country-specific factors, Forex trading is influenced by cross-country ties and a variety of global events. Recent pandemic scenarios such as COVID19 and local elections can also have a significant impact on market pricing. We tested and compared various predictions with external elements such as news items in this work. Additionally, we compared classical machine learning methods to deep learning algorithms. We also added sentiment features from news headlines using NLP-based word embeddings and compared the performance. Our results indicate that simple regression model like linear, SGD, and Bagged performed better than deep learning models such as LSTM and RNN for single-step forecastings like the next two hours, the next day, and seven days. Surprisingly, news articles failed to improve the predictions indicating domain-based and relevant information only adds value. Among the text vectorization techniques, Word2Vec and SentenceBERT perform better. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.