Autoregressive Integrated Moving Average Technique Research Articles

Use of ARIMA model for forecasting fire accidents and physical consequences in the Kingdom of Saudi Arabia

Background Modeling is one of the engineering analyses that can predict most of the accidents and thus reduce damages caused by them. Objective In this paper, an effort is made to forecast the fire accident and physical consequences in the Kingdom of Saudi Arabia (KSA) in the period 2002–2021 by one of the statistical models known as Autoregressive Integrated Moving Average (ARIMA) models. Methods This retrospective investigation performed in the year 2023 within the Kingdom of Saudi Arabia. Information, including injuries and fatalities, as well as fire accidents occurring between 2002 and 2021, was obtained from a database. Those were related to the entire Kingdom of Saudi Arabia. To forecast the variables under examination, the ARIMA model, developed by Box and Jenkins, was employed. Statistical analyses were conducted using Minitab software. Results The results showed that the number of accidents, injuries, deaths, and population are increasing from 2002 to 2021. It is observed that forecasted values of injuries per 100,000 persons and accidents per 100,000 persons due to fire in KSA have increasing trends, and forecasted values of deaths per 100,000 persons, injuries per 1000 accidents, and deaths per 1000 accidents had decreasing trends. Also, the results indicated that the proposed model would be well-fitted to forecast the studied parameters. Conclusions Based on the findings, the ARIMA technique has robust performance and high accuracy for forecasting fire accidents and physical consequences.

Comparative Studies of Hybrid ARIMA and Artificial Neural Network (ANN) Techniques for Predicting Exchange Rate in Pakistan

For predicting time series data the ARIMA and ANN provides a good technique in the field of research. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. When applying the linear models, most existing studies seem to use the same specification for estimation and forecasting, but the dynamic impact of the concerned variables is ignored. In this study combined the ARIMA and Artificial neural network model by adopting both an equal weighted approach and profit weighted approach to capture both th linear and nonlinear components of the exchange rate, and also developed a hybrid techniques by using models of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) and their performance were compared to ANN and Hybrid ARIMA models. The Hybrid models are used for forecasting the future exchange rate for dollar, the exports and imports of data The findings showed that combining both ARIMA and ANN models reap the advantage of linear and nonlinear modeling. The capability of the two models are analyzed based on standard statistical measures such as, mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE). Models effectiveness The effectiveness of the models are analyzed for the foreign exchange rate, imports and exports of the data and concluded that hybrid techniques provided the best forecasting results.

Statistical analysis of gold production in South Africa using ARIMA, VAR and ARNN modelling techniques: Extrapolating future gold production, Resources–Reserves depletion, and Implication on South Africa's gold exploration

South African gold mining is one of the main contributors to the country's economic growth and employment creation. Assessment of future gold production is pivotal for positioning the country's supply security and contribution to the projected global demand. In this study, we modelled and forecasted South African and global gold production trends (1990–2022) using the Autoregressive Integrated Moving Average (ARIMA), Vector Autoregressive (VAR), and Autoregressive Neural Network (ARNN) techniques. The study also evaluated the impact of annual productions on the collated reserves (2008–2021) and the contribution of resource–reserve conversion in sustaining gold reserves within the South African goldfields. The ARIMA (1,1,0) model, based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), outperformed both ARNN (1,1) and VAR (2) methods. The forecast of the ARIMA model showed an increase in South Africa's gold production, with a compounded annual growth rate (CAGR) of ∼0.6%. The model also predicted an increase in global gold production, with CAGR of ∼2.2%. The analyses of reserves–production trends showed that most South African mining companies have been depleting their gold reserves due to production and were minimally converting resources to reserves, averaging at ∼2%/year from 2017 till present. The minimal conversion may also reflect the challenging mining conditions (such as mining depths and underground flooding) and the mining industry's socio-political dynamics. We demonstrated that, if the resource–reserve conversion factor is maintained (at ∼2%/year and above), the forecasted production may sustain the current reserves (at > 2000 t Au excluding tailings) but deplete ∼27 % of the resources to reserves by 2040. However, if the conversion factor continues to decline (to <1 %), the forecasted gold production will reduce the reserves to < 500 t Au with ∼3% of the resources converted to reserves.

A Novel Methodology for Forecasting Business Cycles Using ARIMA and Neural Network with Weighted Fuzzy Membership Functions

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%.

A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal

Rainfall forecasting in urban areas is a significant consideration for city planners due to its connection with urban water management. In this study, the ARIMA (auto-regressive integrated moving average) model, as well as several regression approaches such as simple linear and second to sixth-degree polynomial regression equations, have been used to forecast the annual rainfall based on 120 years of monthly and annual rainfall from 1901 to 2020 in Kolkata Municipal Corporation (KMC), West Bengal. This study compares the performance of ARIMA and other regression techniques in forecasting rainfall using the metrics of R-squared and root mean square error (RMSE). The ARIMA model has been implemented using machine learning techniques in the Python programming language, while additional regression equations have been computed and analyzed using Microsoft Excel 2019. In order to employ the ARIMA model, all assumptions were assessed, and the optimal model order was established using the import auto-Arima package from the pmdarima.arima library. The stepwise model.aic function yielded 0,1,1 as the most suitable order for the model. The findings indicate that, out of all the regression methods employed for rainfall prediction, the fifth-degree polynomial equation exhibits the lowest root mean square error (RMSE), establishing it as the most effective model for rainfall forecasting in this study.

Li-Ion Battery Temperature Forecasting Method: Case-Study

Monitoring and managing battery health is crucial for enhancing performance and lowering running expenses for electronic devices. This paper covers the Deep-learning-enabled temperature forecasting for Li- ion batteries, where they are tested independently. This research presents time series forecasting approaches to predict the temperature of the battery packs. In the proposed model, a Long Short-Term Memory (LSTM) and autoregressive integrated moving average (ARIMA) for predicting the battery temperature and beware of probable future temperatures beforehand to minimize the chances of overcharging and prevent the battery from crossing the threshold value above which battery's health characteristics might get hampered. The growing popularity of data-driven battery prognostics methods shows that ARIMA and LSTM are even when there aren't many prior details available about the batteries. Have a unique dataset of 34 lithium-ion battery packs for this challenge. In one way, the results imply that the existing ARIMA techniques offer interpreting data at various batteries. Having said that, LSTM model outcome recommend that the developed Univariate and Multivariate LSTM model provides finer prediction accuracy in the existence of greater diversification in data for one battery. Thus, try to generalize one forecasting model for each battery type depending on the model's performance.

Do Large Datasets or Hybrid Integrated Models Outperform Simple Ones in Predicting Commodity Prices and Foreign Exchange Rates?

With the continuous advancement of machine learning and the increasing availability of internet-based information, there is a belief that these approaches and datasets enhance the accuracy of price prediction. However, this study aims to investigate the validity of this claim. The study examines the effectiveness of a large dataset and sophisticated methodologies in forecasting foreign exchange rates (FX) and commodity prices. Specifically, we employ sentiment analysis to construct a robust sentiment index and explore whether combining sentiment analysis with machine learning surpasses the performance of a large dataset when predicting FX and commodity prices. Additionally, we apply machine learning methodologies such as random forest (RF), eXtreme gradient boosting (XGB), and long short-term memory (LSTM), alongside the classical statistical model autoregressive integrated moving average (ARIMA), to forecast these prices and compare the models’ performance. Based on the results, we propose novel methodologies that integrate wavelet transformation with classical ARIMA and machine learning techniques (seasonal-decomposition-ARIMA-LSTM, wavelet-ARIMA-LSTM, wavelet-ARIMA-RF, wavelet-ARIMA-XGB). We apply this analysis procedure to the commodity gold futures prices and the euro foreign exchange rates against the US dollar.

Forecasting daily admissions to an emergency department considering single and multiple seasonal patterns

When dealing with several years of daily data, such as the number of daily admissions to a hospital’s emergency department (ED), how complex does it get to forecast into the future? With that in mind, this study has two main goals: to explore the differences between several methodologies, considering both single and multiple-seasonal patterns; and to select the most suitable model for the administration of a Portuguese hospital to use while managing their ED. To that end, we first considered the data as a time series with a single weekly seasonal pattern. We then modelled the data using time series regression, linear regression with autoregressive integrated moving average (ARIMA) errors, seasonal ARIMA and exponential smoothing techniques. Second, the data was set to be a time series with weekly and annual seasonal patterns. Then, using Fourier terms, we applied time series regression, linear regression with ARIMA errors and trigonometric exponential smoothing state space models with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) for the analysis. After selecting the best-fitting models using the Akaike Information Criteria (AIC) values, we forecasted into the future and compared the results using both training and test datasets’ root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values. The time series regression model based on seasonal variables and a weekly seasonal pattern gives the best results. However, we decided to use linear regression with ARIMA errors, seasonal variables, and both weekly and annual seasonal patterns. This produces similar results but allows for the annual seasonality to be considered, which is useful when more data is added.

Forecasting the Inflation in India using ARIMA and Deep Learning Techniques

This Paper Presents forecasting the Inflationof all commodities using Auto Regressive Integrated Moving Average (ARIMA) method, Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and Long Short – Term Memory (LSTM). The data used for estimating the models for the year Apr 2009 to July2020. The appropriate best model is computed by comparing the Mean square Error(MSE), Root Mean square Error(RMSE), and Mean Absolute Percentage Error (MAPE). In this Study the Results Shows that CNN is Performing better model than the other traditional models of ARIMA, MLP, RNN, and LSTM.

Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic

BackgroundInterrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation.MethodsWe propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan.ResultsWe illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0–16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method.ConclusionsOur model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.

Forecasting Covid-19 Transmission with ARIMA and LSTM Techniques in Morocco

In this paper, we are interested to forecast and predict the time evolution of the Covid-19 in Morocco based on two different time series forecasting models. We used Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) models to predict the outbreak of Covid-19 in the upcoming 2 months in Morocco. In this work, we measured the effective reproduction number using the real data and also the fitted forecasted data produced by the two used approaches, to reveal how effective the measures taken by the Moroccan government have been controlling the Covid-19 outbreak. The prediction results for the next 2 months show a strong evolution in the number of confirmed and death cases in Morocco. According to the measures of the effective reproduction number, the transmissibility of the disease will continue to expand in the next 2 months, but fortunately, the higher value of the effective reproduction number is not considered to be dramatic and, therefore, may give hope for controlling the disease.

State of the art in total pulse production in major states of India using ARIMA techniques

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Development of ARIMA technique in determining the ocean climate prediction skills for pre-service teacher

The lack of community understanding of the predictions of the ocean climate is key to addressing the impacts and crisis of the ocean climate. The aims of this study is to explain the stages of ocean climate prediction skills using the ARIMA technique for pre service teacher students as beginner learners and provide solutions to build a conscious and responsive attitude to the ocean climate. The data used is Sea Surface Temperature (SST) Niǹo 3.4, analysis the stages of the Autoregressive Integrated Moving Average (ARIMA) model used MINITAB 16.0 and Microsof Excel that has been developed then applied by 3 respondents. Based on the results of the study, more detailed stages of the arima model and n 3 respondents both got a correlation value of 0.96 for Nino SST 3.4 original value and model value, correlation 0.92 for 1 year validation and correlation 0.99 ENSO prediction for 1 year (12 months) ahead. Thus the development of the ARIMA model stage is very effective to be used to predict climate such as ENSO events and other ocean climate phenomena.

Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting

Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.

Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.

The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.

Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models

Introduction: Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives. Thus, it is crucial to have research for forecast the saline intrusion in Ham Luong River. Our aim was to develop mathematical models in order to forecast the saline intrusion in Ham Luong River, Ben Tre Province.&#x0D; Methods: The Auto regressive integrated moving average (ARIMA) model was built to forecast the weekly saline intrusion in Ham Luong River, which has been obtained from Ben Tre Province's Hydro-Meteorological Forecasting Center over eight years (from 2012 to 2019).&#x0D; Results: The saline concentration increased from January to March and then decreased from April to June. The highest salinity occurred in February and March while the lowest salinity was observed in early June. Moreover, the ARIMA technique provided an adequate predictive model for a forecast of the saline intrusion in An Thuan, Son Doc, and An Hiep station. However, the ARIMA model in My Hoa and Vam Mon might be improved upon by other forecasting methods.&#x0D; Conclusion: Our study suggested that the nonseasonal/seasonal ARIMA is an easy-to-use modeling tool for a quick forecast of the saline intrusion.

A smart system for short-term price prediction using time series models

The primary goal of this paper is to develop a smart system for short-term price prediction for various products using time series models. The system includes a series of processes, e.g., extracting sales data from a website, pre-processing raw data, and using an Autoregressive Integrated Moving Average (ARIMA) model We investigate that traditional ARIMA techniques suffer with performance issues due to identifying the parameter settings therefore, we use auto ARIMA for our project. To evaluate the prediction accuracy of our approach, we use the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) as performance metrics. We test on two seasonal products while considering different brands of each product. The sales data are taken from the PriceMe website. Furthermore, we also compare the ARIMA model with Moving Average (MA) model. In the case of the MA model, we find that the forecast trends are represented by a flat line. Also, the auto ARIMA model is not appropriate for predicting long-term trends.

A Hybrid Model of Autoregressive Integrated Moving Average and Artificial Neural Network for Load Forecasting

The complementary strengths and weaknesses of both statistical modeling paired with machine learning has been an ongoing technique in the development and implementation of forecasting models that analyze the dataset’s linear as well as nonlinear components in the generation of accurate prediction results. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as a hybrid forecasting model for a power utility’s dataset in order to predict the next day’s electric load consumption. ARIMA and ANN models were serially developed resulting to the findings that out of the twelve evaluated ARIMA models, ARIMA (8,1,2) exhibited the best forecasting performance. After identifying the optimal ANN layers and input neurons, this study showed that out of the six evaluated supervised feedforward ANN models, the ANN model which employed Hyperbolic Tangent activation function and Resilient Propagation training algorithm also exhibited the best forecasting performance. With Zhang’s ARIMA and ANN hybridization technique, this study showed that the hybrid model delivered Mean Absolute Percentage Error (MAPE) of 4.09% which is within the 5% internationally accepted forecasting error for electric load forecasting. Through the findings of this research, both the ARIMA statistical model and ANN machine learning approaches showed promising results in being implemented as a forecasting model pair to analyze the linear as well as non-linear properties of a power utility’s electric load data.

Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.