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.

Using Time Series Forecasting for Analysis of GDP Growth in India

Gross Domestic Product is one of the most important economic indicators of the country and its positive or negative growth indicates the economic development of the country. It is calculated quarterly and yearly at the end of the financial year. The GDP growth of India has seen fluctuations from last few decades after independence and reached as high as 10.25 in 2010 and declined to low of -5.23 in 1979. The GDP growth has witnessed a continuous decline in the past five years, taking it from 8.15 in 2015 to 1.87 in 2020.The lockdown imposed in the country to curb the spread of COVID-19 has caused massive slowdown in the economy of the country by affecting all major contributing sectors of the GDP except agricultural sector. To keep on track on the GDP growth is one of the parameters for deciding the economic policies of the country. In this study, we are analyzing and forecasting the GDP growth using the time series forecasting techniques Prophet and Arima model. This model can assist policy makers in framing policies or making decisions.

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.

The Immediate Impact of Covid19 on Tourism Employment in Spain: Debunking the Myth of Job Precariousness?

This note explores the immediate impact that the COVID-19 crisis has had on tourist and non-tourist employment in Spain as a result of the state of alarm and period of confinement decreed from March 14th. The employment and self-employment series drawn from the Social Security affiliation data corresponding to the period between January 2017 and April 2020 are examined using the classical Box–Jenkins method (ARIMA) and the more recent Bayesian Structural Time-Series Models.

Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model - China, December 2019-April 2020.

Introduction: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. Methods: The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R2). Results: The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R2 values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R2 values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively. Discussion: The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model.

COVID-19 Outbreak Prediction using Additive Time Series Forecasting Model

It is no secret that COVID-19 is a hot topic these days. Its spread has engulfed the world. People all throughout the world have suffered because of it. A unique coronavirus epidemic that has swept throughout the globe is examined and analysed in this article. COVID-19 outbreaks in various places are analysed using machine learning models, which are visualised using charts, tables, graphs and predictions depending on the available data. For prediction models, the time series forecasting package (PROPHET) is utilised as part of machine learning. The work done may aid in the development of some novel concepts and ways that can be utilised as recommendations to prevent the spread of COVOD-19. © 2022, Walailak University. All rights reserved.

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).

Analysis of 'earlyR' epidemic model and Time Series model for prediction of COVID-19 registered cases.

The COVID-19 is an epidemic that causes respiratory infection. The forecasted data will help the policy makers to take precautionary measures and to control the epidemic spread. The two models were adopted for forecasting the daily newly registered cases of COVID-19 namely 'earlyR' epidemic model and ARIMA model. In earlyR epidemic model, the reported values of serial interval of COVID-19 with gamma distribution have been used to estimate the value of R0 and 'projections' package is used to obtain epidemic trajectories by fitting the existing COVID-19 India data, serial interval distribution, and obtained R0 value of respective states. The ARIMA model is developed by using the 'auto.arima' function to evaluate the values of (p, d, q) and 'forecast' package is used to predict the new infected cases. The methodology evaluation shows that ARIMA model gives the better accuracy compared to earlyR epidemic model.

Time Series and Statistical Analyses on REIT Stock Prices for Forecasting and Assessing the Impact of COVID-19

In this chapter, we describe the application of Time Series techniques such as ARIMA and ARIMA-GARCH Models to model and forecast the stock prices and the usage of some statistical techniques such as Paired t-Test and Wilcoxon Signed-Rank Test to assess the impact of the COVID-19. The findings provide significant insights into the benefits of mathematical and statistical modeling for real life problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

A log linear Poisson autoregressive model to understand COVID-19 dynamics in Saudi Arabia.

Background: On March 2, 2020, the first case of COVID-19 infection in Saudi Arabia was identified and announced by the health authorities. From first week of March, the number of new confirmed COVID-cases has gradually increased, reaching 2932 confirmed cases on April 9, 2020. A period of increasing infection cases was noticed in June and July 2020. Many methods have been taken to model and predict the new confirmed cases of COVID-19, such as the traditional time series forecasting method and other several methods. Results: We present two statistical models, namely the log linear autoregressive Poisson model and the ARIMA model. The COVID-19 infectious dynamics were evaluated using models in Saudi Arabia, which can affect health, economics, finance, and other fields. We applied both models to daily confirmed cases of COVID-19 count time series data. Moreover, we compare the log linear Poisson autoregressive model with the automatic ARIMA model. Conclusions: The result of this study showed that a log linear Poisson Autoregressive model gives better forecasting and the predicted results of the log linear Poisson Autoregressive model can be used as the baseline for additional interference to avoid future COVID-19 pandemic incidents. Moreover, the application of a log linear Poisson Autoregressive can be comprehensive to other cases in Saudi Arabia.

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.

COVID-19 Crisis: Forecasting the Arab World Economy Performance Using the ARIMA Model

This paper predicts Coronavirus Disease (COVID-19)'s potential influence on the Arab country's economy by using the Autoregressive Integrated Moving Average (ARIMA) model. The world bank offers data of the Arab countries' Gross Domestic Product (GDP) over the period 1960-2019. As we show up at the pinnacle of the COVID-19 pandemic, quite possibly the most critical inquiry going up against us is: what is the potential impact of the progressing crisis on the Arab countries' economic improvement rate? The results have shown that the GDP growth is approximately -3.8% to 1.5% for 2021 and 2022, respectively. The referenced outcomes show that pandemic status significantly affects the Arab world economy special after the energy demand decline, which prompts a fall in oil price. In spite of the fact that the Arab world's financial development is growing again, it is not most likely going to re-visitation of business as usual for quite a while to come. © 2022 Newswood Limited. All rights reserved.

Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up

COVID-19 has spread around the world since it begun in December 2019. The pandemic has created an unprecedented global health emergency since World War II. This paper studies the impact of pandemic and predicts the anticipated casualty rise in India. The data has been extracted from the API provided by https.//www.covidl9ind ia.org/ and covers up the time period from 30th January 2020 when the first case occurred in India till 13th January 2021. The paper provides a comparative study of six machine learning algorithms namely SMOreg, Random Forest, 1Bk, Gaussian Process, Linear Regression, and Autoregressive Integrated Moving Average (ARIMA) in forecasting deceased COVID 19 cases, via the data mining tool such as Weka and R. The major findings show that the best predictor model for anticipating the frequency of deceased cases in India is ARIMA (5,2,0). Utilizing this model, we estimated the propagation rate of deceased cases for the next month. The findings reveal that the fatal cases in India could rise from 151,174 to 157,179 within one month with an average of 190 death reports every day. This study will be helpful for the Indian Government and Medical Practitioners in assessing the spread of pandemic in India and devising a combat plan to mitigate the pandemic.

GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data

In order to promote the economic development of China’s provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China’s provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction.

Forecasting the coronavirus disease 2019 pandemic in India using machine learning and statistical models

COVID-19, which is an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in a massive blow to India with respect to the health of its citizens and economy. The work in this paper focuses on the Prophet model, linear regression model, Holt's model and the ARIMA model for predicting the number of confirmed, recovered cases, deaths and active cases along with growth rate, recovery rate and mortality rate in India for the month of November 2020. The performance of all the above mentioned models has been evaluated using standard metrics namely R2, adjusted R2, root-mean-square error and mean absolute error. © 2022 Inderscience Enterprises Ltd.