Autoregressive Integrated Moving Average Approach Research Articles

Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This article proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP SVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDP SVD_ARIMA approach is effective and efficient in predicting missing healthcare data.

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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</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.

Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index.

This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

Prediksi Indeks Bursa Efek Indonesia 2023 Pendekatan ARIMA, Machine Learning dengan R Programming

The Indonesian Stock Exchange Index (BEI) is the main indicator of the Indonesian stock market. The problem in this research is a specific issue to solve the practical problem of predicting the future movement of the BEI index which has strategic value for investors, traders and companies in Indonesia. The aim of this research is to be able to predict the BEI index until the end of 2023, because the stock exchange plays a big role for the Indonesian economy as an economic and financial function. Traditional approaches such as ARIMA (Autoregressive Integrated Moving Average) have been used to predict the movement of the BEI index. However, in recent years, machine learning and data mining techniques have become popular as more sophisticated alternative approaches. This research uses a combined approach between ARIMA and machine learning with R Programming. Daily IDX index data from January 2012 to December 2022 will be taken from Yahoo Finance. This data will then be cleaned and processed using R Programming. The ARIMA approach will be used as a baseline to compare machine learning performance. The results focus on the estimated closing stock prices for the next 365 days or the average until the end of 2023. The Time Series value of the possible minimum/maximum predicted value for IDX shares in 2023 is a minimum predicted value of 6786,212 - 6849,559, a maximum predicted value of 6850,093 - 7086,012. Trends represent a good approach in predicting the future direction of closing prices.

Modeling and Forecasting Unemployment Rate in Tanzania: An ARIMA Approach

Purpose: This study aims to develop a reliable forecasting approach for Tanzania's unemployment rate and provide policymakers with an effective tool for decision-making. Unemployment forecasting is vital for informed policymaking, particularly in countries like Tanzania. Methodology: This study employs a quantitative research design and adopts Box Jenkin's methodology and the ARIMA (AutoRegressive Integrated Moving Average) model for unemployment forecasting in Tanzania. The entire available dataset for the specified period is utilized, employing a non-probability sampling technique. Diagnostic tests, including ACF (AutoCorrelation Function), PACF (Partial AutoCorrelation Function), and unit root analysis, are conducted to guide the optimal model selection. Differencing addresses non-stationarity in the time series data by removing trend and seasonality effects. The optimal model selection is based on criteria such as AIC (Akaike Information Criterion), Schwartz, and Hannan-Quinn. Findings: The study finds that the ARIMA (3,1,4) model demonstrates superior performance in forecasting the unemployment rate in Tanzania. Diagnostic checks validate the adequacy of the model, revealing white noise residuals. The forecasts indicate a consistent downward trend in unemployment rates over the next nine years, suggesting potential labour market improvements in Tanzania. These findings enhance our understanding of Tanzania's unemployment dynamics and provide valuable insights for policymakers. Originality/Value: The study lies in its application of Box Jenkin's methodology and the ARIMA model to unemployment forecasting in Tanzania. By utilizing the entire available dataset and employing diagnostic tests for model selection, the study enhances the reliability of the forecasting approach. The study offers policymakers an informed decision-making tool by providing accurate forecasts and capturing underlying trends.

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

Synthesizing forecasts to inform decision‐making and advance ecological theory

Synthesizing forecasts to inform decision‐making and advance ecological theory

Short term load forecasting based on ARIMA and ANN approaches

Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the demand expected by the grid, many smart meters are required to collect sufficient data. However, the problem is multi-dimensional and simple power aggregation techniques may fail to capture the relational similarities between the various types of users. Therefore, accurate forecasting of energy demand plays a key role in planning, setting up, and implementing networks for the renewable energy systems, and continuously providing energy to consumers. This is also a key element for planning the requirement for storage devices and their storage capacity. Additionally, errors in hour-to-hour forecasting may cause considerable economic and consumer losses. This paper aims to address the knowledge gap in techniques based on machine learning (ML) for predicting load by using two forecasting methods: Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN); and compares the performance of both methods using Mean Absolute Percentage Error (MAPE). The study is based on daily real load electricity data for 709 individual households were randomly chosen over an 18-month period in Ireland. The results reveal that the (ANN) offers better results than ARIMA for the non-linear load data.

Forecasting of Red Chilli Prices in Banyumas Regency: The ARIMA Approach

Chilli is one of the national strategic commodities in Indonesia. The price of chilli tends to fluctuate every season which may contribute to inflation. In June 2022, chilli price contributed to the food group inflation as 0.24%. At the same period, in Banyumas it contributed as 0.20%. This study aimed: 1) to analyse the performance of red chilli price in Banyumas, 2) to analyse the forecasting of red chilli prices in Banyumas. This study used a descriptive method for assessing performance of red chilli price and method for forecasting of red chilli prices using the Autoregressive Integrated Moving Average (ARIMA) model. The secondary data of the average price of chilli used in the study was obtained from PIHPS. There were 69 data from April 2017 until December 2020. The red chilli prices in Banyumas Regency shows an upward trend in a fluctuating during April 2017 to December 2022. ARIMA 1.11 has been estimated as the most suitable model for predicting prices. The result showed that the price of chilli in Banyumas during January-December 2023 was predicted as 39.703 to 42.815 rupiahs per kilo, and it tends to increase every season.

Economic Scratch on the Future Growth Path of Bangladesh: An ARIMA Approach

The purpose of this study is to investigate the future growth path (growth rate) of Bangladesh. Bangladesh is one of the fastest-growing nations in the world. To secure sustainable and spontaneous economic development, contentious increasing economic growth is a prerequisite. In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used to track down the future growth path of Bangladesh. Time series data from year 1972 to 2018 has been utilize to predict the economic growth for year 2019 to 2028. By using the ARIMA approach it has been found that next 10 years (2019-2028) the GDP growth of Bangladesh will be reduced from the current growth rate. The average GDP growth rate from the year 2019-2026 will be clustered near 5% and in the year 2027 it will be peak by 8.77% and the following year growth rate will be consolidated by 1.39%.

The Validity of Autoregressive Integrated Moving Average Approach to Forecast the Spread of COVID‐19 Pandemic in Africa

Africa’s first COVID‐19 case was recorded in Egypt on February 14, 2020. Although it is not as expected by the World Health Organization (WHO) and other international organizations, currently a large number of Africans are getting infected by the virus. In this work, we studied the trend of the COVID‐19 outbreak generally in Africa as a continent and in the five African regions separately. The study also investigated the validity of the ARIMA approach to forecast the spread of COVID‐19 in Africa. The data of daily confirmed new COVID‐19 cases from February 15 to October 16, 2020, were collected from the official website of Our World in Data to construct the autoregressive integrated moving average (ARIMA) model and to predict the trend of the daily confirmed cases through STATA 13 and EViews 9 software. The model used for our ARIMA estimation and prediction was (3, 1, 4) for Africa as a continent, ARIMA (3, 1, 3) for East Africa, ARIMA (2, 1, 3) for West Africa, ARIMA (2, 1, 3) for Central Africa, ARIMA (1, 1, 4) for North Africa, and ARIMA (4, 1, 5) for Southern Africa. Finally, the forecasted values were compared with the actual number of COVID‐19 cases in the region. At the African level, the ARIMA model forecasted values and the actual data have similar signs with slightly different sizes, and there were some deviations at the subregional level. However, given the uncertain nature of the current COVID‐19 pandemic, it is helpful to forecast the future trend of such pandemics by employing the ARIMA model.

Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches

It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.

Assessment of Tourism in Nepal with Reference to Regional Countries: Trend Analysis and ARIMA Approach

Tourism (either domestic or international or both) is an internationally flourished business or industry all over the world. The economic foundations of tourism are essentially the cultural assets, the cultural property and the nature of the travel location. So, it has a greater contribution to the country’s balance of payments. Simple trend analysis was carried out using a set of line graphs along simple linear regression. For forecasting of international tourist arrivals of period: 1962-2020, and real per capita international tourist receipts of period: 1995-2018, the suitable Auto-Regressive Integrated Moving Average (ARIMA) models were developed using Akaike Information Criterion along with method of autocorrelation function and partial autocorrelation function.&#x0D; Nepal has a significant growth rate of 1.372 of the international tourist arrivals. It has the eighth position for international tourist arrivals among nine counties. Likewise, Nepal has a significant growth rate of 1.315 of real per capita international tourist receipts. It has the fourth position for real per capita international tourist receipts among nine counties.&#x0D; Nepal has been receiving its international tourist arrivals, growth as well as real per capita international tourist receipts. Forecasts of international tourist arrivals of Nepal are 879638.3 in 2018, 860459.0 in 2019, 875824.1 in 2020, 891189.3 in 2021, and 906554.4 in 2022. Forecasts of real per capita international receipts in dollars are 687000000 in 2019, 727000000 in 2020, 807000000 in 2021, and 845000000 in 2022.

Projecting Insurance Penetration Rate in Nigeria: An ARIMA Approach

Insurance industries' market penetration level is revealed by the premiums paid as a proportion to overall economic output. Nigeria's insurance penetration rate is 0.33 per cent despite its population advantage of about 200 million people. This rate is far below the average African rate of 2.98 per cent, and the lowest compared to some Sub-Sahara African countries like Kenya, Zimbabwe, Namibia and South Africa. The paper uses annual data on insurance penetration (IP) for the period 1981 to 2018. Autoregressive Integrated Moving Average (ARIMA) model was used to projects the insurance penetration rate for 12 years (2019 to 2030). The result shows that the insurance penetration rate keeps decreasing but at a slower rate. The paper recommends that the national regulatory bodies of insurance such as National Insurance Commission of Nigeria (NAICOM) should innovate and implement policies. This action will pave ways for the insurers to design insurance coverage, which will meet the specific needs of heterogeneous societies in order to spur insurance penetration rates.

The Forecasting of Electrical Energy Consumption in Morocco with an Autoregressive Integrated Moving Average Approach

The national demand for primary energy has experienced an average increase of almost 5% in recent years, driven by the growth in electricity consumption, which grew by an average of 6% per year between 2003 and 2017, by virtue of the almost generalization of rural electrification and the dynamism of our economy and especially the policy of major works in infrastructure, industry, agriculture, tourism, and social housing. In fact, forecasting the demand for electrical energy remains a controversial issue in the development of the electricity grid and energy management. The ARIMA (1, 1, 1) model is applied to model electrical energy consumption for the annual period from 1971 to 2020. The same data are also used to predicting for 2021–2030 in order to verify the adequacy of the model and to provide information on the state of energy demand in Morocco in the future. The main results indicate an upward trend in electrical energy consumption by the end of 2030, with electricity consumption expected to be in the range of 2039639.09–53589.00 GWh per year.

Predicting Crude Oil Prices During a Pandemic: A Comparison of Arima and Garch Models

The unprecedented global turn of events primarily due to the spread of highly contagious corona pandemic has led to a substantial fall in crude oil prices. A forecast for crude oil prices is important as oil is required for all major economic activity, particularly production and transportation. This study aims to apply two commonly used methods of Autoregressive Inte-grated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) to predict the WTI crude oil prices for the period February 10, 2020, to April 27, 2020. Such a comparative analysis of these methods in unprecedented times is missing in the existing literature. ARIMA suggests ARIMA (4,1,4) model while GARCH (1,1) as the best among their own respective family of models. And between ARIMA and GARCH ARIMA model is recommended for forecasting as it has a lower root mean squared error (RMSE) and mean absolute error (MAE). The study recommends using a mean based ARIMA approach for predicting future values in extreme situations.

Time Series Analysis on Selected Rainfall Stations Data in Louisiana Using ARIMA Approach

Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)12), Abbeville (ARMA (0,0,1) (0,0,2)12), Monroe Regional (ARMA (0,0,1) (0,0,0)12), New Orleans Airport (ARMA (1,0,0) (0,0,2)12), Alexandria (ARMA (1,0,1) (0,0,0)12), Logansport (ARIMA (0,1,2) (0,0,0)12), New Orleans Audubon (ARMA (1,0,0) (0,0,0)12), Lake Charles Airport (ARMA (2,0,2) (0,0,0)12) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.

Impact of Seasonality and Influenza Rates on Interventions to Reduce Hospital-Acquired Clostridioides difficile Rates

Background: Hospital-acquired Clostridioides difficile infection (HA-CDI) rates are highly variable over time, posing problems for research assessing interventions that might improve rates. By understanding seasonality in HA-CDI rates and the impacts that other factors such as influenza admissions might have on these rates, we can account for them when establishing the relationship between interventions and infection rates. We assessed whether there were seasonal trends in HA-CDI and whether they could be accounted for by influenza rates. Methods: We assessed HA-CDI rates per 10,000 patient days, and the rate of hospitalized patients with influenza per 1,000 admissions in 4 acute-care facilities (n = 2,490 beds) in Calgary, Alberta, from January 2016 to December 2018. We used 4 statistical approaches in R (version 3.5.1 software): (1) autoregressive integrated moving average (ARIMA) to assess dependencies and trends in each of the monthly HA-CDI and influenza series; (2) cross correlation to assess dependencies between the HA-CDI and influenza series lagged over time; (3) Poisson harmonic regression models (with sine and cosine components) to assess the seasonality of the rates; and (4) Poisson regression to determine whether influenza rates accounted for seasonality in the HA-CDI rates. Results: Conventional ARIMA approaches did not detect seasonality in the HA-CDI rates, but we found strong seasonality in the influenza rates. A cross-correlation analysis revealed evidence of correlation between the series at a lag of zero (R = 0.41; 95% CI, 0.10–0.65) and provided an indication of a seasonal relationship between the series (Fig. 1). Poisson regression suggested that influenza rates predicted CDI rates (P &lt; .01). Using harmonic regression, there was evidence of seasonality in HA-CDI rates (2 [2 df] = 6.62; P &lt; .05) and influenza rates (2 [2 df] = 1,796.6; P &lt; .001). In a Poisson model of HA-CDI rates with both the harmonic components and influenza admission rates, the harmonic components were no longer predictive of HA-CDI rates. Conclusions: Harmonic regression provided a sensitive means of identifying seasonality in HA-CDI rates, but the seasonality effect was accounted for by influenza admission rates. The relationship between HA-CDI and influenza rates is likely mediated by antibiotic prescriptions, which needs to be assessed. To improve precision and reduce bias, research on interventions to reduce HA-CDI rates should assess historic seasonality in HA-CDI rates and should account for influenza admissions.Funding: NoneDisclosures: None

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87% for the FY2020 and projected forecasting for the import demonstrates a negative variation of -1.61% for the FY2020. The findings of the undertaken study recommend the policymakers of Pakistan to take corrective measures to increase exports and to prevent the country from the trade deficit. The policymakers of Pakistan should give more incentives to the exporters and decrease the cost of doing business to be more competitive than the regional economies such as India, Bangladesh, and China.