ARIMA Model based Time Series Modelling and Prediction of Foreign Exchange Rate against US Dollar

Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data. © 2022 IEEE.

Exploring excess mortality during the covid-19 pandemic with seasonal arima models

This chapter analyses the daily and the weekly deaths in Germany, Sweden and Spain between 2016 and 2019. It gives an estimate of the future number of deaths in 2020 in those countries, with a special focus on uncertainty, and thereby presents alternative models and methods for estimating the excess mortality in 2020, the year of the Covid-19 pandemic. Suitable seasonal auto-regressive integrated moving average (ARIMA) models are sought that allow the best possible fit to the available time series, in the sense that the properties of the resulting residual processes are compatible with those of white noise. It can be seen that only deaths in the age class 0-30 can be satisfactorily presented by a binomial mortality model. ARIMA is one of the most widely used forecasting methods for predicting univariate time series data. © ISTE Ltd 2022.

COVID-19 Prediction Using Time Series Models

Real-time data has evolved to become an integral part of understanding events across different timelines. Machine Learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COVID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the SARS-CoV-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and VARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COVID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and VARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using Voila. © 2022 IEEE.

Time Series: Predicting Nigerian Food Prices using ARIMA Model and R-Programming

The majority of food commodities in Nigeria have seen persistent price instability. this is brought by elements like insecurity/insurgency, poor storage facilities, seasonal price changes, inconsistent government policies, COVID-19 containment measures, poor access to credit, technical inputs, lack of modern farm tools and implements. This study focused on comparing the prices of four different food items - beans, onion, tomato, and yam using the ARIMA model to forecast future prices. Two out of the six geopolitical zones of Nigeria were used for the study;the North-Central and North-West. The National Bureau of Statistics (NBS) provided the raw data between 2017 and 2018, and the items were weighed in kilograms (Kg). The data was extrapolated into a time series data by executing in R Studio. The stationarity of the series data was obtained by a Unit root Test using the KPSS test (If p<0.05 means the time series is stationary). Results from the forecasted values indicated that food commodities' prices increase with time, making ARIMA a good model for forecasting prices. It was recommended that necessary measures should be put in place to ameliorate the high cost of food prices being experienced in the country of Nigeria. © 2022 IEEE.

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.

Epidemiological Modeling with Sir and Arima Dynamic Models and Machine Learning to Predict the Future Behavior of Covid-19 in Panama

Mathematical models SIR and ARIMA were used, within an epidemiological approach, to adjust them to the COVID-19 pandemic data in Panama to establish a scientific criterion for taking decisions for the effects control that this pandemic has brought. Based on the predictions made from the adjustments of these models, it was concluded that they can be adjusted correctly to the data, allowing to make short-term predictions in a satisfactory way, however, if a more accurate model were to be carried out, independent variables could be included, besides time, such as mobility restrictions. This work lays down the foundations for future investigations of epidemiological models in Panama due to its exposition of mathematical model's comparison used to analyze the behavior of the COVID-19 Pandemic. Jupyter Notebook, GitHub, Machine Learning libraries and mathematical software such as Wolfram Mathematica were used. Adjustment of data was performed through statistical techniques and, for this prediction, statistical software Minitab and E-Views were also used. © 2022 IEEE.

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.

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Vaccine Supply Optimization and Forecasting using Random Forest and ARIMA Models

In order to tackle the Corona Virus Disease, it took a considerable amount of time for the governments to come up with effective and efficient vaccines. After the vaccines were developed, the next challenge was to supply the vaccines to various designated centers based on demographics, population distribution, and other factors. The whole system for vaccine supply played a vital role during the COVID-19 pandemic. We also saw a lot of haphazard and mismanagement in some places especially when the cases per day surged high, as people weren't prepared for such a situation. Now that we have got enough data, we can use it to optimize the vaccine supply across various Covid Vaccination Centers and be prepared for any such circumstances in the future. In this paper, we have proposed a two-step approach where considering the past supply and wastage data we performed a classification task that indicates whether doses are to get wasted at a given center. If yes, we then perform demand forecasting based on the number of administered doses so that the wastage can be reduced, and supply can be optimized. © 2022 IEEE.

Did COVID-19 Pandemic Influence the Ether Cryptocurrency Market? Perception and Analysis

In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of attention in the global market. The value of such currencies is increasing on a daily basis and peaked during the pandemic. One of these significant cryptocurrencies is the ether cryptocurrency, which ranks second only to the bitcoin cryptocurrency in terms of the value of a single coin. The ARIMA model will be used to forecast the price of ether. In this paper, an hourly forecast and a short term period forecast are performed. The forecast clearly shows that the ARIMA model performed better on log transformed data than on the original data. It's also evident that COVID-19 pandemic has also aided the growth of ethereum when compared to previous year. © 2022 IEEE.

Prediction of the Spread of Covid-19 in Indonesia Using the SEIRD Model and Hybrid Model with ARIMA Correction

Building an SEIRD segmentation model with life dynamics, estimating its parameters, and utilizing the ARIMA model to determine and predict the difference between the SEIRD model solution and the observed and fitted data constitute the machine learning approach used in this study. We use machine learning techniques to accomplish this. A hybrid method is used to process the newly collected data, using the model predictions and the residuals from the data. The historical values of the infected, recovering, and dying populations divided by the confidence intervals serve as the parameters for the SEIRD model, which in turn serve as parameters for the models' applicability. confidence level range. Long-term and short-term estimates with confidence intervals can be provided by the model, which can assess incoming data in real-time. We tested the model's predictions using actual data on COVID-19 cases in Indonesia. All current models are tested with the maximum allowed deviation during validation using MAE, MSE, MLSE, normalized MAE, and normalized MSE. The findings shown that the features of the current model are highly accurate for infected patients, patients who are recuperating, and patients who have passed away. Governments, business, and policy makers can use the findings of our suggested model to forecast controllable health hazards. This methodology can also be applied to additional research to progress science. © 2022 IEEE.

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.

Omicron BA.2 Prediction Research Based on SEIR-ARIMA Mixed Model

Omicron BA.2, a new variant of severe acute respiratory syndrome coronavirus (SARS-CoV-2), has attracted worldwide attention due to its high infectivity and vaccine escape mutation. Based on the SEIR model being susceptible to changes in external factors and having specific errors, the ARIMA model is data-dependent and can only capture linear relationships. In this paper, based on the traditional infectious disease dynamic model SEIR and the differential integrated mean autoregressive model ARIMA, an SEIR-ARIMA mixed model is proposed to predict and evaluate the virus outbreak in March in Jilin Province, China. The data from SEIR and ARIMA models were processed using SPSS to obtain the predicted values f and e, respectively. Linear regression modeling was performed on the predicted values f and e to establish the SEIR-ARIMA model. MATLAB is used to complete the best linear fitting line. Furthermore, The results show that the model's predicted value is in good agreement with the actual value. It shows that the SEIR-ARIMA mixed model based on the SEIR-ARIMA model has a good prediction effect, which is beneficial for the country to make the right decision when facing the epidemic. It is of great value for preventing other types of infectious diseases in China in the future. © 2022 IEEE.

Comparison of the SVR and ARIMA models for Prediction of daily imported new cases of COVID-19 in Shanghai, China

The outbreak and spread of COVID-19 poses a tremendous threat to the health of people all over the world. We collected the new imported COVID-19 cases daily in Shanghai, China from September 1, 2021 to January 17, 2022 from the National Commission on Health of the People's Republic of China website. The SVR and ARIMA models were constructed and compared. On this base, it is provided for the early warning of the outbreak of COVID-19 and the targeted preventive measures proposed for this infectious disease. © 2022 IEEE.

The Application of ARIMA Model in Retailing Industry and Forecasting on Economic Trend of Industry

The COVID-19 pandemic has severely impacted our lives, and many industries are experiencing instability. We are curious to see whether retailing businesses, one of the most robust industries in US, have been affected, and if affected, how have the industry been affected. In order to gain the required insight, we first acquire stock prices of six representative retailing companies in US, then we apply ARIMA model on the data to forecast their trends in the near future, which will imply the general robustness of the industry. This procedure includes testing the stationary of time series data of stocks, and finding the suitable ARIMA parameters for each stock, using various methods. Accuracy metrics are brought into discussion to determine how accurate our forecast is. Finally, we draw the conclusion that ARIMA model, being a suitable method for our case of study, has given us desirable result: the stocks of 6 selected retailing companies will perform steadily with slight increase at the end of the year. There are several practical values or our research: By applying ARIMA models on stocks of retailing companies, we discovered that application of such models on retailing industry is not only pragmatic, but effective;and the results of our analysis provide future researchers with insight of economy of this era of pandemic. © 2022 SPIE.

Prediction of novel coronavirus pneumonia epidemic trend based on ARIMA model

In order to improve the speed and efficiency of the Department of epidemic prevention and control, this paper uses ARIMA model to train and fit the number of confirmed cases on the basis of the historical epidemic diagnosis information of Guangdong Province. By dealing with the stability of time series, determining the parameters of ARIMA model and testing residual white noise, the ARIMA model is established to predict the number of confirmed epidemic cases, and the number of confirmed epidemic cases in March may 2021 in Guangdong Province is accurately predicted, so as to help the epidemic prevention and control departments improve the accuracy and effectiveness of epidemic control. © 2022 SPIE.

Predicting COVID-19 future trends for different European countries using Pearson correlation.

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases. During the past 18 months, many nations have watched countries whose COVID-19 cases have risen sharply, in anticipation of handling the situation themselves. However, this did not provide accurate results, as no research was conducted that compared countries to determine if their COVID-19 case trends were correlated. As official data on COVID-19 cases has become increasingly available, using the Pearson correlation technique to pinpoint the countries that should be closely monitored will help governments plan and prepare for the number of infections that are expected in the future at an early stage. In this study, a simple and real-time prediction of COVID-19 cases incorporating existing variables of coronavirus variants was used to explore the correlation among different European countries in terms of the number of COVID-19 cases officially recorded on a daily basis. Data from selected countries over the past 76 weeks were analysed using a Pearson correlation technique to determine if there were correlations between case trends and geographical position. The correlation coefficient (r) was employed for identifying whether the different countries in Europe were interrelated, with r > 0.85 indicating they were very strongly correlated, 0.85 > r > 0.8 indicating that they were strongly correlated, 0.8 > r > 0.7 indicating that they were moderately correlated, and r < 0.7 indicating that the examined countries were either weakly correlated or that a correlation did not exist. The results showed that although some neighbouring countries are strongly correlated, other countries that are not geographically close are also correlated. In addition, some countries on opposite sides of Europe (Belgium and Armenia) are also correlated. Other countries (France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden and Turkey) were either weakly correlated or had no relationship at all.

ARIMA Model to Predict the Covid-19 Pandemic Cases in Telangana State

According to World Health Organization, India faced 28,307,832 confirmed cases as on June 2, 2021. The first wave of Covid-19 has taught new lessons to human lives in the year 2020. The second wave enveloped the India keeping the nation in second place globally with the confirm cases. Telangana has cases of 583,228 as of June 2, 2021. In this study ARIMA (1, 0, 17) model was found to be the best model among the other two to predict the cases. © 2021 IEEE.

Two Applications of Machine Learning on COVID-19

In December 2019, a new virus called COVID-19 broke out, and in 2020, it rapidly spread all over the world. The fast rate of the spread of the virus and high mortality have brought severe harm to the health of people and the economy of almost all countries around the world. Therefore, the virus has become the object of much researches. As the study moving on, treatment and vaccine have become the leading research directions at present. For treatment, measures should be taken to protect the most severe patients to reduce the death rate, and thus we are supposed to find patients with more serious illnesses. The decision tree and Xgboost are used to get the mathematical model about protease (an essential index in judging the severity of the disease) and realize the visualization of protease data. For vaccine, we solve the problem of predicting COVID-19 Vaccination Progress in the world in 2021 using the ARIMA model, which is obtained through the mean of time-series. Eventually, we got 10-day and 3-month vaccination forecasts. © 2022 SPIE

Exploring COVID-19 Progression Patterns

Background: A novel coronavirus COVID-19 causing acute illness with severe symptoms, represents the causative agent of a contagious potentially lethal disease. COVID-19 was declared as pandemic by WHO. Aims: This Research aims to study the COVID-19 outbreaks in the fifteen most impacted countries in the world, find the relationship between the precautionary measures of governments and COVID-19 confirmed cases and deaths, and to forecast the pandemic in the following short time. Methods: The global numbers of confirmed cases and deaths of COVID-19 were obtained from the European Union Data. The data of governmentsʹ response actions for COVID-19 were estimated using the Oxford study. Box-Jenkins methodology, ARIMA model, R package were used in data analysis. Results: The rate of COVID-19 confirmed cases is 0.4 per thousand, and the death case rate is 0.03 per thousand of the world population. The rate of death cases was the lowest in Brazil, and the highest in Spain. The usefulness of precautionary measures and its effect on the number of confirmed cases and deaths in the different countries were estimated. A high correlation was established concerning the applied measurements and time of application. The model used for forecasting the expected cases was consistent with our tested result, while the model for forecasting death showed a fair consistently. Conclusion: We conclude that the health system must be reviewed, and these precautionary measures evaluated whether they are beneficial or more stringent conditions should be imposed. © RJPT All right reserved.