ARIMA Research Articles

Assessing and anticipating environmental challenges in Lahore, Pakistan: future implications of air pollution on sustainable development and environmental governance.

Urban environment and air quality are changing primarily due to land use land cover (LULC) changes, economic activity, and urbanization. Air pollution has been increasingly acknowledged as a major issue for cities due to its extensive effects on health and well-being. As the second most populous city in the country, Lahorefaces alarming levels of air pollutants, whichinduced this study to focus on the pervasive issue of air pollution in Lahore. For this, the study collected air pollutants data from the Environmental Protection Department of Punjab and analyzed them using the ARIMA model. In the research results, both the observed data and predictive models uncovered concerning trends in pollutant concentrations, ultimatelyportraying a concerning picture for air quality management. Carbon monoxide (CO) levels show a consistent rise, surpassing Pakistan's environmental standards by 2025. Similarly, nitrogen dioxide (NO2) concentrations escalate, exceeding prescribed standards. Ground-level ozone (O3) also demonstrates a substantial increase, surpassing standards by 2025. Both PM2.5 and PM10 exhibit marked upward trends, projected to exceed recommended limits, particularly PM10 throughout the study year. The Air Quality Index exhibits an observable upward trend, fluctuating between 70 and 442 from 2015 to 2020. Similarly, a positive correlation was found between population growth and land use conversion into residential areas. Projections suggest a continuous increase, potentially hitting a severe level of 500 during winter by 2025. These findings point to an impending air pollution crisis, demanding urgent action to address the hazardous situation in the city. The study recommends that urban air pollution should be reduced, and the negative health effects of air pollution should be minimized using vegetation barriers, screens, and greening initiatives. Strict regulations and monitoring initiatives need to be put in place in big cities to monitor pollution and vegetation.

Experimental Research on Prediction of Remaining Using Life of Solar DC Centrifugal Pumps Based on ARIMA Model

In order to improve the stability and reliability of the solar DC centrifugal pump real-time operation and prevent the centrifugal pump failure caused by the unexpected shutdown of the system, a set of accurate and efficient centrifugal pump condition monitoring systems was built. A time series-based strategy for predicting the remaining using life (RUL) of centrifugal pumps was proposed. The time series of head and efficiency of centrifugal pumps at specific flow conditions were measured, the corresponding failure thresholds were set, and different differential autoregressive integrated moving average (ARIMA) models were developed to predict the remaining useful life of the pumps. The results show that the maximum prediction error of the head ARIMA model established under the design conditions of the pump was 0.040%, and the head time series reaches the failure threshold of 8 m at the 653rd data point; the maximum prediction error of the efficiency ARIMA model was 0.042%, and the efficiency time series reaches the failure threshold of 16% at the 672nd data point. According to the proposed prediction strategy, the RUL of the centrifugal pump under the design condition was 53 h. The head time series of the pump at high flow conditions reaches a failure threshold of 5 m at the 640th data point; the efficiency time series will reach a failure threshold of 12.5% at the 578th data point, and the RUL of the centrifugal pump at high flow conditions was 78 h. The established ARIMA model has a high prediction accuracy and can effectively predict the RUL of centrifugal pumps.

ARIMAX-Threshold 모형을 이용한 20대 이하 마약류 사범 검거 수 시계열 분석

In recent years, the number of drug offenders under 20s has surged, accounting for approximately 35.5% of all drug offenders in 2023, which becomes a serious social issue in Korea. The purpose of this work is to study and compare time series models for statistical analysis and prediction for the number of drug offenders under 20s. ARIMA and ARIMAX model is applied to the data of the number of drug offenders arrested under 20s. In order to further improve the predictability, a model added by threshold, reflecting the presence or absence of the exogenous variable, is proposed and compared. As for the exogenous variable, dark web data that is directly related to the number of drug offenders arrested, as a main drug dealer, is used. Based on cross-correlation coefficient analysis, the ARIMAX-Threshold model including indicator variables and threshold is proposed. Out-of-sample forecasting analysis is performed to compare the forecasting performance, and it is revealed that the ARIMAX-Threshold model proposed in this study improves the forecasting errors by up to 9.4%. The predicted values and prediction intervals of the data on the number of drug offenders under 20s until April 2025 are presented, which will contribute to help in policy development for drug crime prevention.

Predictive Modelling of Network Capacity Demands: A Machine Learning Approach for Global Enterprise Backbone Networks

In the dynamic landscape of global enterprise networks, accurate capacity forecasting is paramount for ensuring optimal resource allocation and preventing service disruptions. This paper presents a hybrid machine learning methodology that combines Autoregressive Integrated Moving Average (ARIMA) models with additional techniques to enhance the accuracy and reliability of network capacity forecasts. By leveraging historical traffic data and incorporating external factors, we develop a predictive model that outperforms traditional methods and adapts to the evolving demands of modern networks. The effectiveness of our approach is validated through rigorous testing against established benchmarks, demonstrating significant improvements in forecasting accuracy.

ARIMA-Based forecasting of monthly rainfall in Mandi district, Himachal Pradesh

ABSTRACT To anticipate monthly rainfall time series, autoregressive integrated moving average (ARIMA) modelling was performed using R studio. For a period of 30 years (in mm), the monthly rainfall data were gathered for eight localities in the Mandi district of Himachal Pradesh: Bharol, Chachiyot, Jogindernagar, Karsog, Kataula, Mandi, Sarkaghat, and Sundernagar. The augmented Dickey–Fuller test was performed to determine whether the rainfall data were steady before applying the ARIMA model. The minimal values of the Akaike information criterion, Bayesian information criterion, autocorrelation function, and partial autocorrelation function were used to select the best models. The best fit was determined to be ARIMA (0,0,2) (2,1,0)12 for five stations and ARIMA (0,0,0) (1,1,0)12 for the remaining three stations. A box test is performed to validate the ARIMA model and verify the autocorrelation in the data. Using the ARIMA model, forecasts are made for the 10 years (2021–2030). The validity and performance of the ARIMA models were evaluated using error statistics (mean absolute error = 34.65, root-mean-square error = 33.58, and R2 = 0.93). The projected data's mean and standard deviation were close to the actual data. As a result, the ARIMA model's monthly rainfall prediction parameters were satisfactory.

Hybrid firefly algorithm–neural network for battery remaining useful life estimation

Abstract Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions.

Forecasting of Inland Fish Production: Case of Catla (<em>Catla catla</em>) and Gift Tilapia (<em>Oreochromis niloticus</em>) Production in Udawalawa Reservoir in Sabaragamuwa Province, Sri Lanka

This study focused on assessing and forecasting Catla (Catla catla) and Tilapia (Oreochromis niloticus) production in the Udawalawe reservoir in Sri Lanka using an 11-year time series data from 2010 to 2021. The relevant secondary data were collected from the National Aquaculture Development Authority of Sri Lanka (NAQDA). Unit root tests, Dickey-Fuller tests, and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and trend analysis were applied for the data analyses. According to the results, stationarity behaviour was observed in both Catla and Tilapia production information series. Hence, SARIMA (2,2,1) (1,0,1)12 is the appropriate empirical model for forecasting Catla production. Furthermore, the maximum average landings of Catla production were observed in the March-June time period. SARIMA (1,0,1) (1,1,1)12 was indicated as the most suitable model to forecast Tilapia production in the reservoir. The maximum average of Tilapia production was also observed in the March-June period. Both the SARIMA model and trend analysis gave approximately equal production information regarding Catla and Tilapia. Thus, these findings showed that the SARIMA model has more accuracy than trend analysis when analysing the time series information for short durations. However, when the duration becomes larger, forecasting is not suitable using the SARIMA model, and trend analysis gives a clear view up to a certain level. Both Catla and Tilapia production exhibited significant changing patterns and multiple seasonalities. Thus, trend analysis gives valuable insights than SARIMA model in long term predictions in case of this study. These findings are highly important for enhancing fish production in this reservoir, as they predict future fish catches under the current reservoir conditions.

Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling

Jilin Province, a key agricultural hub in Northeast China, has long been impacted by climate change, with drought disasters significantly affecting its agricultural output and ecological environment. Accurate drought prediction is essential for the effective utilization of water resources and agricultural production. This study proposes a novel drought prediction model that utilizes the SSA-VMD (Sparrow Search Algorithm Optimized Variational Mode Decomposition) technique to decompose meteorological data, followed by the reconstruction of the decomposed components using four entropy algorithms, including approximate entropy. The model integrates ARIMA (AutoRegressive Integrated Moving Average) and BiLSTM (Bidirectional Long Short-Term Memory network) for forecasting the reconstructed components, subsequently combining their outputs. In terms of spatio-temporal data, the study employs prediction models including the spatio-temporal cube, spatio-temporal hotspot analysis integrated with empirical kriging, and local outlier analysis to examine spatial distribution. The model’s predictive performance is validated from three perspectives: statistical characteristics of the indicators, comparison between predicted and observed values through prediction curve plots, and box plots. The results demonstrate that the combined SSA-VMD-ARIMA-BiLSTM model significantly enhances prediction accuracy compared to single models, as exemplified by its application in Changchun City. The model achieved an R2 of 0.938 and a root mean square error (RMSE) of 0.047 in drought prediction, outperforming the single ARIMA model (R2: 0.636, RMSE: 0.709) and the BiLSTM model (R2: 0.514, RMSE: 0.901). Additionally, across the entire province, the model’s R2, MAE, and RMSE are 0.82, 0.15, and 0.083, respectively, suggesting that the model exhibits not only high prediction accuracy but also a degree of generalizability. Furthermore, the results from the spatio-temporal cube, spatio-temporal hotspot analysis, and local outlier analysis demonstrate the method’s high accuracy and stability in predicting both short-term and long-term droughts. Particularly in short-term drought prediction, the model effectively captures the spatio-temporal distribution characteristics of short-term meteorological droughts. This study offers new methodological support for enhancing the early warning capabilities of drought risk in Jilin Province, providing a robust foundation for addressing the challenges posed by climate change. The findings not only address certain shortcomings in current drought prediction research but also introduce new methodologies and perspectives for future studies.

The Utilization of Time Series Data Forecasting Techniques on Hospitals Pharmaceutical Inventory

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.

Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Time Series Modeling and Forecasting of Finger Millet Cultivation Area, Production and Productivity in Chhattisgarh, India: The Box Jenkins Methodology

Chhattisgarh has taken important steps towards promoting millets cultivation and improving the livelihood of farmers. To increase millets production in Chhattisgarh, the state government launched the Millet Mission in September 2021. This mission has been started with a view to make Chhattisgarh the ‘millet hub of India’. The present study was conducted on time series modelling and forecasting of finger millet crops in Chhattisgarh India using Box Jenkins methodology and used historical data on currently cultivated area, production and yield of finger millet crops. The time series data was collected from 2001 to 2023, and analysis of the study was carried out using path analysis and Box Jenkins ARIMA model; and among various 20 models the best and suitable ARIMA (0, 1, 2), (3, 1, 1) and (2, 0, 4) model was selected based on AIC, BIC, MAPE, RMSE, MAE. With the help of the selected appropriate model, the cultivation area, production and yield of finger millet cultivation in Chhattisgarh was forecasted for the year 2024 to 2030. But marvellous, diminishing and fluctuating trend was observed in finger millet cultivation area and production over the forecast period. Whereas increasing and stochastic trend was observed in finger millet yield over the forecast period.

Water–Energy–Food Nexus in the Yellow River Basin of China under the Influence of Multiple Policies

The water–energy–food (WEF) nexus constitutes a pivotal aspect of regional ecological protection and high-quality development. The exertion of multiple WEF-related policies would engender both synergies and trade-offs within the WEF nexus. However, a quantified framework that integrates the impact of multiple WEF-related policies with conventional WEF nexus assessments and simulations is currently lacking. This study quantified the WEF nexus in the Yellow River basin (YRB) of China under the influence of multiple policies, calculated the current and future WEF scores under different policy combination scenarios using the improved entropy weight method, the auto-regressive integrated moving average (ARIMA) model, and the linear optimization method. The results revealed the following: (1) From 2000 to 2020, WEF overall scores and subsystem scores were substantially increased with spatial heterogeneity. (2) Scenario analysis indicated that policy implementation would generally accelerate WEF score improvements in each city, yet embracing all policies simultaneously was not optimal for each city. (3) The spatial heterogeneity in policy impacts was also found in the YRB, with higher trade-offs in the upper reaches of cities, and higher synergies in the middle and lower reaches of cities. To attain high-quality development within the YRB, the related policies’ implementation should consider the regional disparities and enhance the optimization of resource allocation across the regions.

Longer-term impacts of the COVID-19 pandemic on obstructive sleep apnoea (OSA)-related healthcare: a province-based study

RationaleFollowing marked reductions in sleep medicine care early in the COVID-19 pandemic, there is limited information about the recovery of these services. We explored long-term trends in obstructive sleep apnoea...

Statistical Model for the Occurrence of Typhoid Fever in Takum, Taraba State, Nigeria: An Arima Model Approach

Statistical Model for the Occurrence of Typhoid Fever in Takum, Taraba State, Nigeria: An Arima Model Approach

Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.

Implementation of the ARIMA model for prediction of economic variables: evidence from the health sector in Brazil

In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly minimizing further economic setbacks. Contemporary literature introduces various forecasting models, such as the autoregressive integrated moving average (ARIMA) model. Recognized for its statistical alignment with numerous linear models, the ARIMA model has demonstrated its efficacy across various domains. This paper delves into applying the ARIMA model to predict five critical economic time series that substantially influenced Brazil’s public and private healthcare sectors throughout the economic crisis between 2000 and 2020. These time series encompassed the variables (i) the gross domestic product—GDP, (ii) the Extended National Consumer Price Index—IPCA, (iii) the unemployment rate, (iv) the total number of health plan beneficiaries, and (v) total number of individual health plan beneficiaries. Importantly, this study provides a comprehensive outline of the ARIMA implementation process, underscoring that precise forecasting is pivotal for managers aiming to curtail financial anomalies and avert resource shortages. The findings highlight the ARIMA model’s (1, 0, 2), (2, 2, 1), (0, 1, 2), (1, 1, 2), and (2, 2, 1) viability in accurately forecasting health-related time series, exceeding 95% accuracy for economic variables analyzed. These results have significant practical implications for healthcare managers and decision-makers. By offering accurate forecasts of critical economic metrics, such as the unemployment rate and the transition of beneficiaries between public and private health systems during economic downturns, this research provides valuable insights for strategic planning within the healthcare sector.

Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Model experimental studies on active heave compensation control strategy for electric-driven offshore cranes

Ship-mounted heave compensation offshore cranes are indispensable for isolating connected payloads from the support vessel during lifting operations under harsh sea conditions. In this paper, an innovative adaptive robust control strategy is presented for the electric-driven active heave compensation (EDAHC) system, combining an equivalent model predictive control (EMPC) method with a bias proportional integral derivative (BPID) framework to effectively mitigate the adverse effects of wave-induced heave motions from the support vessel on the suspended payload. Building upon the inherent field-oriented control in the permanent magnet synchronous motor (PMSM), the BPID-based control structure is introduced, motivated by its prompt responsiveness and robust resistance against model discrepancies and irregular disturbances. Facilitated by a torque compensation mechanism, the EMPC-based control scheme, synthesized with an autoregressive integrated moving average (ARIMA)-based heave motion prediction algorithm, is subsequently developed to achieve nonlinear friction deadzone correction and adaptive regulation of BPID parameters, thereby ensuring optimal performance of the EDAHC system within specified state and input constraints. Comparative experimental tests conducted on a scaled EDAHC testbed validate the superior capabilities of the proposal in station-keeping, position tracking, constraint satisfaction, and robustness against parametric uncertainties.

Accurate and efficient daily carbon emission forecasting based on improved ARIMA

Major nations across the globe are increasingly concerned about the rising trends in carbon dioxide (CO2) emissions, particularly in societies of varying scales. Against this backdrop, precise prediction of carbon emissions becomes critically important, especially for the formulation and adjustment of near-term carbon reduction policies. However, the non-linear, non-stationary, and complex nature of daily carbon emission data poses a great challenge for daily-level forecasting especially in the big data context. To address this issue, we propose a novel composite forecasting approach named DCEF dedicated to the estimation of daily carbon emissions. In concrete, our approach employs the Empirical Mode Decomposition (EMD) for data stabilization and the Auto-regressive Integrated Moving Average (ARIMA) model for forecasting, while integrating the Truncated singular value decomposition(TSVD) technique for data compression and mitigating noise. Finally, DCEF is empirically validated with real daily carbon emission datasets collected from 6 sectors in 13 countries of varying sizes. Experimental results demonstrate the advantages of our approach compared to other baseline models in terms of prediction accuracy and efficiency.