Predictive Analytics in Emergency Services

This paper compares the predictive performance of time series forecast methods on the Nigerian Naira/US Dollar (NGN/USD) and The Gambian Dalasi/US Dollar (GMD/USD) exchange rates. The forecast methods—Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES), Holt’s Linear Trend, and Damped Holt—were applied to the annual Nigerian Naira and Gambian Dalasi against the US Dollar for the period 1960–2020. The best model for forecasting exchange rates in both countries was selected based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE). The findings in this study revealed that both Naira/US Dollar and Gambian Dalasi/US Dollar exchange rate distributions are positively skewed and ARIMA (0,2,2) model was selected as the most appropriate model for forecasting both exchange rates. The results also showed that by 2030, the Nigerian Naira/US Dollar exchange rate will rise by 37.06 percent while the Gambian Dalasi/US Dollar will rise by 23.18 percent. This study suggests that both countries should adopt tighter fiscal, monetary, and supply-side policies.

Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.

An optimized model using LSTM network for demand forecasting

Predictive analytics has become a crucial tool in modern business decision-making, particularly in demand forecasting. By leveraging historical data and statistical modeling techniques, businesses can enhance resource allocation, optimize inventory management, and improve overall operational efficiency. Among various predictive analytics methods, time series models are widely employed due to their ability to capture trends, seasonality, and patterns in demand fluctuations. This explores the application of time series models, including Moving Average (MA), Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (Holt-Winters method), and advanced deep learning models such as Long Short-Term Memory (LSTM) networks. These models enable businesses to make data-driven decisions by accurately predicting future demand, thereby reducing costs and improving supply chain responsiveness. Furthermore, the integration of predictive analytics in business resource allocation extends beyond inventory management. It influences workforce planning, financial forecasting, and supply chain optimization, ensuring that organizations align their operations with anticipated market trends. However, despite the benefits, challenges such as data quality issues, model interpretability, external disruptions, and scalability concerns remain key obstacles in implementing time series forecasting. Emerging advancements in artificial intelligence (AI), big data analytics, and cloud-based forecasting solutions are shaping the future of predictive analytics. The integration of real-time data and machine learning models offers new opportunities for businesses to enhance forecasting accuracy and agility. As demand forecasting continues to evolve, organizations must adopt a strategic approach to model selection and implementation to fully capitalize on the advantages of predictive analytics. This study provides a comprehensive overview of predictive analytics for demand forecasting, highlighting the role of time series models in optimizing business resource allocation and discussing future trends that will drive further innovation in this field.

The article deals with the theoretical aspects of the market demand method selection criteria and their application in practice. As the definition of market demand and conditions is related to the achievement of important general strategic decisions and consumers’ behaviour on the competitive market, the accurate identification of the market demand method selection criteria and their application can reduce decision indefiniteness. Hence, the main aim of the article is to analyse market demand forecast method selection criteria, and apply them in developing Lithuanian furniture demand forecast. The theoretical part of the article starts with the analysis of the forecast method reasoning and forecast method classification under various economic literature criteria. The analysis of the main forecast method groups disclosed that it was rather difficult to determine the advantage of any method in forecast estimation, whereas, always the risk of the wrong method selection remained. It was determined that usually unanimous authors’ opinion on the complex application of quantitative and qualitative forecast methods differed in two aspects: one group of researchers supported the priority of quantitative forecast methods by using qualitative forecast methods as an auxiliary or alternative means, while the other group of authors stated that equal application of both types of forecast methods guaranteed higher reliability and carried more information. Quite often any market demand forecast encounters the relevant forecast method selection problem. It should be noted that there are advantages and disadvantages of the forecast method selection in every case; therefore, the analysis and differentiation of the main forecast method selection criteria is expedient. The selection of the forecast method should be based on several criteria taking into account the applicability of the forecast method complexity, i.e., forecast accuracy level, period of time, the scope of initial data, forecast costs, and the level of result appropriateness and applicability. The article deals with the research related to the selection of the forecast method for the Lithuanian furniture industry to define the furniture demand forecast for 2007. The object of research is furniture sales on the national market that reflect the solvent part of the furniture demand. The comparison of the forecast accuracy assessment indicators that were estimated by using different forecast methods indicate that the lowest furniture sales forecast error values were achieved by applying exponential smoothing method where &=0.3: forecast error value (et = 77.19) demonstrated the smallest difference between furniture sales forecast and fact, mean absolute percentage error (MAPE = 8.29 proc.) indicated high forecast accuracy, and mean percentage error (MPE = 8.29 proc.) described small positive forecast deviation. In order to achieve the qualitative forecast assessment, forecast reliability intervals were calculated where criterion z = 1.960. The results allow to assume that furniture sales forecast for 2008 with 95 per cent probability can vary from LTL, 844.64 mio. to LTL, 1147.20 mio.

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

This paper reviews sales forecasting methods and its ability to be used in modern enterprise resource planning systems. Having Dynamics 365 Business Central as an example was reviewed for the practical application of such methods, including those using machine learning techniques. During the described work existing solution based in time series algorithms was investigated and analyzed in detail, suggestions were made regarding the clustering techniques usage.Enterprise resource planning is defined as an ability to deliver the business software that unifies processes and data models within various areas such as finance, human resource management, supply chain management, manufacturing, distribution, etc. This paper highlights the problems that can be solved using this type of software, namely: automate the processes that were not formalized before; unify data captured by different departments for its better analysis; collect, group, and aggregate information for analytical and reporting purposes. Pros and cons for building its own solutions compared to implementation of solutions from reliable vendors are presented. Top vendors on the market are identified.Benefits of accurate sales forecasting and its importance to the business are identified: preventing missed sales, decreasing storage costs, improving marketing activities, decreasing write-offs, and improving cash flow. From another side, the common sales forecasting techniques are listed and described: clustering, descriptive, factor, time series, and regression analysis methods. Machine learning is considered as a prospective option for efficient automation of the analytical processes.Next, the specific implementation of enterprise resource planning system – Dynamics 365 Business Central – is reviewed in scope of the sales forecasting problem. It is identified that the ability of the standard solution is limited with Sales Budgeting functionality that can support manual sales forecasting but lacks automation for the future sales figures. However, its system can be extended using Sales and Inventory Forecast which in its turn can utilize the benefits of artificial intelligence techniques by means of Microsoft Azure Machine Learning service. Existing experiment that was reviewed in scope of this paper, had been based on Microsoft Time Series algorithm and combined the best results from the following techniques: autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and seasonal and trend decomposition using LOESS (STL).The following potential areas for improvement were identified in the existing approach: for large item portfolio the seasonality may significantly differ from one group of items to other; the same approach is applied to items on different life cycle stage; there could be a huge difference in behavior of slow and fast moving items; non-moving items may impact the overall picture.As a potential improvement, the usage of the clustering analysis methods is proposed. The author suggests splitting items into clusters using k-means machine learning method before performing time series analysis and assessing the impact of this approach on a large verified data set.Manuscript received 10.06.2020

Abstract-The paper describes the statistical methodology of multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) methods for mid term load forecasting of the country. The mid term load forecast has many applications such as maintenance scheduling, fuel reserve planning and unit commitment. However, the monthly peak load is a nonlinear, and non-stationary signal. Therefore, this paper proposed a statistical methodology to solve this problem which using multiple linear regression, and autoregressive integrated moving average, based on historical series of electric peak load, weather, and new economic variables such as consumer price index, and industrial index. This paper focuses on the forecasting of monthly peak load for 12 months ahead. This study focused on the mid term load forecasting of peak load demand for Thailand. Finally, we compared between MLR and ARIMA method that the results obtained the autoregressive integrated moving average method proves to be the best accuracy more than the multiple linear regression method.

PT. MI is one of the companies engaged in the manufacturing sector. Rear Fender is a product that produce by PT. MI. The problems for this research is the difference between demand customer and orders which is too far resulting in the remaining stock, with the average of stock is 8,33%. Inventory cost can increase because of excess stock. This aims of the study is to find the best forecasting method for rear fender products. The forecasting method is Moving Average, Weighted Moving Average, and ARIMA. The forecasting method is depend on demand data plots. The result of demand data plots it horizontal plotting so the forecasting method is time series method. The best Forecast for rear fender products is the ARIMA with the ARIMA model (1,1,0). The ARIMA method was chosen because the MAPE values are 13.305, MAD 5435.45 and MSE 41988401 which are the smallest when compared to the 2 other time series forecasting methods.

Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, or Winters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results.

Short term load forecasting is essential for energy management of any power system. Over the years, a variety of methods such as exponential smoothing, autoregressive integrated moving average (ARIMA) and Artificial Neural Networks (ANNs) have been proposed in the literature. All of these methods have been used in conjunction with similar days approach to bolster the prediction accuracy. However, there is no general consensus in literature on what is the best approach to group 365 days of a year into different categories. In contrast, similar months approach to forecast electricity demand has received little attention although it alleviates many of the efforts in sorting similar days into separate groups. In this paper, we propose a similar months approach based seasonal ARIMA (SARIMA) to forecast electric demand both on a national and a household scale. Through comparison with similar days based approach on similar tasks, we demonstrate that this method is a viable method for short term electric load forecasting of power systems as well as in building energy management applications.

Di situasi saat ini dimana era pandemic operasional perusahaan berjalan dengan kekuatan organisasi dengan para pelaku manajemen yang berpikir keras bagaimana mengelola permintaan serta kebutuhan dan memasok kebutuhan kepada para pelanggannya. Penulisan artikel ini membahas tentang pengelolaan manajemen perusahaan dengan menggunakan metode peramalan permintaan dalam memenuhi permintaan para intermiten produk suku cadang pada sebuah perusahaan otomotif, dengan sebuah penelitian permintaan intermiten perusahaan yang menunjukkan pola fluktuatif, adanya permintaan dalam jangka waktu tertentu, dan penelitian ini menguji 2 metode peramalan dalam memprediksi permintaan suku cadang yaitu metode Metode ABC, Simple Exponential Smoothing (SES) dan Moving Average Exponential Smoothing dengan menggunakan data permintaan 2017-2020 untuk meramalkan permintaan pada tahun 2021. Kinerja metode peramalan ditentukan berdasarkan mean squared error, dan hasil penelitian ini menunjukkan bahwa penggunaan metode Simple Exponential Smoothing memiliki performansi yang paling baik. Tujuan utama dari penelitian ini adalah untuk membandingkan dan memilih metode peramalan terbaik untuk memprediksi kebutuhan dan permintaan kebutuhan suku cadang perusahaan. In the current situation where the pandemic era has caused many companies to experience crises, the company's operational activities are carried out with organizational strength that makes management actors think hard in managing the needs and supplying the needs of their customers. This study discusses the management of the company by using the demand forecasting method in meeting the demand for intermittent spare parts products in an automotive company. In this study, the company's intermittent demand trend shows a fluctuating pattern with demand within a certain period of time. This study examines 2 forecasting methods in predicting the demand for spare parts, namely ABC analysis, the Simple Exponential Smoothing (SES) method and the Moving Average Exponential Smoothing using 2017-2020 demand data to forecast demand in 2021. The performance of the forecasting method is determined based on the mean squared error, and the research results This shows that the use of the Simple Exponential Smoothing method has the best performance. The main purpose of this study is to compare and choose the best forecasting method to predict the needs and demands of the company's spare parts needs

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed.
 Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

This study proposes a hybrid forecasting method that combines ARIMA and SVM models to address the complexity of supply chain demand forecasting. The ARIMA model is used to capture linear trends and seasonal fluctuations in time series, while the SVM model is responsible for modeling the nonlinear residual part. By combining the advantages of both, this paper builds a more accurate demand forecast model. Experimental results show that the combined model performs well when dealing with large market fluctuations and non-linear demand changes. Compared with the ARIMA and SVM models applied individually, the forecast error is significantly reduced. The model shows good prediction accuracy in multiple categories such as food, household products and electronic products, provides a more comprehensive demand forecast solution for supply chain management, and helps improve the company's inventory management and market response capabilities.

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different iś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.