Forecasting gold prices in India using an ARIMA model

Over the years, the gold prices have been increasing rapidly. Covid-19 and its impact leads to rise in the prices of Gold in the year 2020. So many variables are mindful for increasing the gold cost in India and it leads to investment decisions of individuals and enterprises. Autoregressive Integrated Moving Average (ARIMA) is useful method to gauge time series data. In the paper we mainly focus on daily costs of Gold from the year 2018 to 2020 to determine and forecast the daily gold prices in 2021. Also estimate the error (%) between the observed and estimated values through ARIMA model. This study will provide the estimates of suitable ARIMA model (0,1,2) along with Autocorrelation function (ACF) & Partial autocorrelation function (PACF) from the selected data, The auxiliary source information shows the positive patterns for getting effectiveness, For quantitative examination and speculation selections of financial backers. © 2023 Author(s).

Effects of cross-border E-commerce customs declaration ceiling increase on export performance under COVID-19 conditions

Purpose: This study examines the impact of raising the ceiling value of Electronic Commerce Custom Declarations (ECCD) on Turkey's export performance processed via ECCD during the COVID-19 period. Design/methodology/approach: This paper examines the impact of the pandemic conditions on Cross-Border Electronic Commerce (ECCD) exports from Turkey to 47 countries over 42 months before and during the pandemic. An empirical analysis using the Pooled Mean Group (PMG) and Mean Group (MG), Panel Autoregressive Distributed Lag (ARDL) approach was conducted to identify the factors affecting export flows. Findings: The findings suggest that raising the ceiling of the ECCD trade is a vital factor in increasing exports. and this result is robust after controlling for pandemic conditions. On the other hand, although the COVID-19 shock mitigates the export volume of ECCD in the short run, it changes by reversal and increases the export level in the long run. Additionally, the number of COVID-19 cases and deaths in Turkey have a significant and negative impact on export flows in the short run, while they have a positive and significant effect in the long run. Practical implications: The results of this study have practical implications for policymakers, emphasizing the potential and significance of Cross-Border E-Commerce (CBEC) trade. Originality/value: The study is a pioneering effort in the literature of CBEC to explore how changes in the upper limit on customs declarations can affect export flows, taking into account the impact of the COVID-19 pandemic. © 2023, Emerald Publishing Limited.

Influence of the global environment on capital flows in the London Office market

PurposeThe London office market is a major destination of international real estate capital and arguably the epicentre of international real estate investment over the past decade. However, the increase in global uncertainties in recent years due to socio-economic and political trends highlights the need for more insights into the behaviour of international real estate capital flows. The purpose of this study is to evaluate the influence of the global and domestic environment on international real estate investment activities within the London office market over the period 2007–2017.Design/methodology/approachThis study adopts an auto-regressive distributed lag approach using the real capital analytics (RCA) international real estate investment data. The RCA data analyses quarterly cross-border investment transactions within the central London office market for the period 2007–2017.FindingsThe study provides insights on the critical differences in the influence of the domestic and global environment on cross-border investment activities in this office market, specifically highlighting the significance of the influence of the global environment in the long run. In the short run, the influence of factors reflective of both the domestic and international environment are important indicating that international capital flows into the London office market is contextualised by the interaction of different factors.Originality/valueThe authors provide a holistic study of the influence of both the domestic and international environment on cross-border investment activities in the London office market, providing more insights on the behaviour of global real estate capital flows.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

Study of COVID19 Impact on Moroccan Financial Market Based on ARDL

The aim of this work is to study the impact of the COVID-19 pandemic new cases on the Moroccan financial market using the Autoregressive Distributed Lag (ARDL) approach. The analysis focuses on the relationship between the natural logarithm of the Moroccan All Shares Index (MASI) price and the natural logarithm of new daily cases of COVID-19 in the short term as well as in the long term. A cointegration test is performed on the daily time series for the period from March 3, 2020 to February 11, 2022. A causality test of Toda-Yamamoto is also applied on the variables. The implementation of the forecast with the ARDL method improves the forecast accuracy by 8% to achieve 26.7%. The implementation of the forecast with the ARDL method shows that the addition of the lag of COVID19, the trend and the seasonality makes it possible to achieve a MAPE of 26.7% by improving it by 8% compared to the forecast with the lag of the price only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Application of Time Series Analysis Methods to Assess the impact of COVID-19 on Carbon Dioxide Emission Reduction in Connecticut

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.

Analyzing pure contagion between crude oil and agricultural futures markets

Crude oil and agricultural product prices are important factors affecting a country's economic and social stability. The pure contagion between these two markets may lead to excessive price linkage, increasing the fragility of the financial system. This paper uses the CEEMDAN method, fine-to-coarse reconstruction method, and TVP-VAR model to study the pure contagion between crude oil and agricultural futures markets. The empirical results show that there always is significant pure contagion between agricultural futures markets. However, pure contagion between crude oil and agricultural futures markets only exists in some specific periods. The crude oil futures market has obvious pure contagion to the agricultural futures markets in most periods. Only a few periods the agricultural futures have pure contagion to the crude oil futures. It is worth noting that the COVID-19 epidemic aggravates the pure contagion between crude oil and the agricultural futures markets. Based on the research conclusions, this paper puts forward corresponding policy recommendations, hoping to provide a reference and theoretical basis for the government to formulate corresponding policies. © 2023 Elsevier Ltd

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

The revise is given as follows: The rapid emergence of the super-spreader COVID-19 with severe economic calamities with devastating social impact worldwide created the demand for effective research on the spread dynamics of the disease to combat and create surveillance systems on a global scale. In this study, a novel hybrid Deterministic Autoregressive Fractional Integral Moving Average (ARFIMA) model is presented to forecast the bimodal COVID-19 transmission dynamics. The heterogeneity of multimodal behavior of the COVID-19 pandemic in Pakistan is modeled by a hybrid paradigm, in which a deterministic pattern is combined with the ARFIMA model to absorb the inherent chaotic pattern of the pandemic spread. The fractional fluctuation of the real epidemic system is effectively taken as a paradigm by stochastic type improved the deterministic model and ARFIMA process. Special transformations are also introduced to enhance the convergent rate of the bimodal paradigm in deterministic modeling. The outcome of the improved deterministic model is combined with the ARFIMA model is evaluated on the spread pattern of pandemic data in Pakistan for the next 30 days. The performance-indices of the hybrid-model based on Relative-Errors and RMSE statistics confirmed the effectiveness of the proposed paradigm for long-term epidemic modeling compared to other classical and machine learning algorithms. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Time series analysis of the number of tuberculosis cases in tangshan area

Objective The time series analysis model was used to predict and warn the number of tuberculosis (TB) cases in Tangshan area in different time, which provided a reference for scientific prevention and control of TB epidemic in this area. Methods The number of monthly TB cases in Tangshan from January 2005 to December 2021 was collected, and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict the number of TB cases in 2022. Meanwhile, the difference between the predicted number of TB cases and the actual observed number of TB cases in the area was explored during the period of COVID-19 in 2020 by this model and rank test. Results From January 2005 to December 2021, the ARIMA (1, 1, 0) (1, 1, 2)s model was fitted well with the actual observed number of TB cases (AR =-0. 530, ARs =-0.967, MAs = 0. 861, P0. 05;Stationary R2 = 0. 558, R2 = 0. 634, BIC = 7. 887;Ljung-Box Q = 25. 605, P 0. 05), with peaks TB incidence in March, April, and December every year, and the predicted number of TB cases in 2020 was 1 800. From 2005 to 2019, ARIMA (1, 1, 0) (0, 1, 2)s model was fitted well with the actual number of cases (AR =-0. 544, ARs =-0. 840, MAs = 0. 697, P 0. 05;Stationary R2 = 0. 582, R2 = 0. 621, BIC = 7. 939;Ljung-Box Q = 24. 211, P 0. 05), with peaks TB incidence in March, April, and December every year, and the predicted number of TB cases in 2020 was 1 985. The observed and predicted number of TB cases from January 2020 to May 2020 were statistically significant (Z =-2. 023, P0. 05). Conclusion It is necessary to increase the intensity of early warning of TB in March, April, and December every year in Tangshan to prevent the epidemic of TB. At the same time, the coordination of the staff of TB prevention institutions and the emergency system should be strengthened during the epidemic situation of COVID-19, and effectively ensure the registration and medical treatment of TB patients during the epidemic situation. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Predicting the upshot of covid-19 on crude-oil prices in nigeria using mlparima model

Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy. © 2022 IEEE.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.

Performance of Traffic Accidents' Prediction Models

Modeling traffic-accident frequency is a critical issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objectives of this study are to model traffic road accidents, fatalities and injuries in Jordan, using different modeling techniques, including regression, artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models and to evaluate the safety impact of travel-restriction strategies during Covid-19 pandemic on traffic-accident statistics for the year 2020. To accomplish these objectives, data of traffic accidents, registered vehicles (REGV), population (POP) and economic gross domestic product (GDP) from 1995 through 2020 were obtained from related sources in Jordan. The analysis revealed that accidents, fatalities and injuries have an increasing trend in Jordan. Root mean of square error (RMSE), mean absolute error (MAE) and coefficient of multiple determination (R2) were sued to evaluate the performance of the developed prediction models. Based on model performance, the ANN models are the best, followed by the ARIMA models and then the regression models. Finally, it was concluded that the strategies undertaken by the government of Jordan to combat Covid-19, including complete and partial banning of travel, resulted in a considerable reduction of accidents, injuries and fatalities by about 35%, 37% and 50%, respectively. © 2023, Jordan University of Science and Technology. All rights reserved.

Lightweight IoT-Based System for COVID-19 Patient Health Monitoring and Prediction

With the present Coronavirus disease (COVID-19) pandemic, Internet of Things (IoT)-based health monitoring devices are precious to COVID-19 patients. We present a real-time IoT-based health monitoring system that monitors patients' heart rate and oxygen saturation, the most significant measures necessary for critical care. Specifically, the proposed IoT-based system is built with Arduino Uno-based hardware and a web application for retrieving the patients' health information. In addition, we implement the Autoregressive Integrated Moving Average (ARIMA) method in the back-end server to predict future patient measurements based on current and past measurements. Compared to commercially available devices, the system's results are adequately accurate, with an acceptable RMSE for predicted value. © 2022 IEEE.

Analysing the Impact of Covid-19 on Automobile Sales in India Using ARIMA Modelling

The Covid-19 pandemic has caused severe economic depression and has disrupted the supply chains of various industries. The automobile industry which contributes significantly to the Indian economy was gravely hit due to the lockdowns, semiconductor shortage and the uncertainty associated with the pandemic. This research paper analyses the effect of Covid-19 on the automobile sales in India using the time series modelling approach. The data recorded by SIAM from 2012 to 2019 was used to develop the Autoregressive Integrated Moving Average (ARIMA) model following the Box-Jenkins methodology. ARIMA model (2, 1, 3) was chosen as it had the lowest AIC and BIC criteria. This model was used to forecast the sales from 2020 to 2021 to give a picture of the expected automobile sales had the pandemic not occurred. The forecasted data from the model developed has then been compared with actual automobile sales data during the pandemic to gauge the level of impact Covid-19 had on the Indian automobile industry. The paper also explores the associated challenges that the automobile industry had to face due to the pandemic. © 2022 The authors and IOS Press.

Analysis and Forecasting of the COVID-19 Epidemic Curve

Corona Virus Disease 2019 (COVID-19) has emerged as a supreme challenge for the whole world as well as India. As of now approximately 6.5 million people died in the world. However, the major setback to the world was in 2021 as a result of the second and third waves of COVID-19, which were caused by a different variation of COVID-19 than the first variant. The governments and health sectors were not aware of the subsequent possible waves due to the lack of data analysis competency and improper forecasting models. Hence finding an inflection point of this epidemic curve for COVID-19 infection and death is very imperative to understand different waves and variants instigating these waves. Similarly predicting the epidemic curve for the future is vital to make the government and the systems aware of the impending situation and make them prepare accordingly. Hence this work attempts to demonstrate conditions for finding inflection points and intervals which helps in finding the number of waves and the variants of COVID-19. Simultaneously the forecasting of the number of infections in forthcoming wave is also done using the auto-regressive integrated moving average model to identify the number of waves in India. The prediction of the two months data was compared with actual data for proper analysis. © 2022 IEEE.

Real Time Captioning and Notes Making of Online Classes

Due to the COVID-19 pandemic, all activities have turned online. The people who are hard of hearing are facing high difficulty to continue their education. So, the presented system supports them in attending the online classes by providing the real time captions. Additionally, it provides summarized notes for all the students so that they can refer to them before the next class. Google Speech to Text API is used to convert the speech to text, for providing real time captions. Three text summarization models were explored, namely BART, Seq2Seq model and the TextRank algorithm. The BART and the Seq2Seq models require a labelled dataset for training, whereas the TextRank algorithm is an unsupervised learning algorithm. For BART, the dataset is built using semi supervised methods. We evaluated all these models with rouge score evaluation metrics, among these BART proves to be best for our dataset with the following scores of 0.47, 0.30, 0.48 for rouge-1, rouge-2 and rouge-l respectively. © 2022, IFIP International Federation for Information Processing.

The Effects of COVID-19 and Workplace Mobility to Stock Price and Exchange Rate in Indonesia: An Econometric Approach

The COVID-19 pandemic has reached its 20th month in Indonesia and still damaged various sectors, particularly economy. The policies imposed by the government impacted mainly the stock price. exchange rate, and people mobility in Indonesia. However, there are limited studies that incorporate these variables in Indonesia context. Thus, this study investigates the relationship between the COVID-19 pandemic, stock price, exchange rate, and workplace mobility simultaneously. This study employs Vector Autoregressive (VAR) as the analysis considering its advantages in finding the causal relationship between variables and periodic interpretation using Impulse Response Function (IRF). The VAR results show that from the Granger Causality Test, it turns out that the shocks from COVID-19 positivity rate and mobility in workplaces caused the changes in stock price and exchange rate. On the other hand, the IRF results exhibit the depreciating responses of stock price and exchange rate due to the shocks of COVID-19 positivity rate and mobility are enormous in the short term. In the longer term, the stock price response needs a longer time to return to the initial condition than the exchange rate. Therefore, further policy evaluation and formulation become essential to maintain the stock price and exchange rate, mainly due to the effect of COVID-19 and workplace mobility. © 2022 IEEE.

Approaching SMM4H with auto-regressive language models and back-translation

We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (OGNLP) participated in the sub-task: Classification of tweets self-reporting potential cases of COVID-19 (Task 5). For our submissions, we employed systems based on autoregressive transformer models (XLNet) and back-translation for balancing the dataset. © 2021 Association for Computational Linguistics.