Exponential Smoothing Model Research Articles

Forecasting real exchange rate (REER) using artificial intelligence and time series models

Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set.

A Brief Review on Queue Management Systems for different Applications

Queue management technology helps to reduce actual and predicted customer wait times, improve customer satisfaction, and provide the data to your managers needed to further optimize service, Queue Management System (QMS)presents a viable solution for different applications. It is employed to manage lines in a queue area in a variety of circumstances and locales. The article discusses the concepts of Queue Management Systems for Hospitals, Satellite Networks Based on Traffic Prediction, using Deep Neural Networks (DNN). Managing high patient loads in tertiary care hospitals represents a significant challenge in streamlining health service delivery. At several hospital service locations, including the registration, lab, and bill payment counters, patients must frequently wait in line. In these circumstances, Queue Management Systems (QMS) offer a practical patient management option. Satellite Internet the Adaptive Random Early Detection (ARED) queue management algorithm is proposed to be improved by traffic prediction based on a dynamic triple exponential smoothing model, smoothing coefficient optimization of the model using a differential evolutionary algorithm, and a cubic function based on traffic prediction. Customers and the administrative staff of the company can use a queue management system that uses the Open-CV platform and CNN algorithm for image processing, together with real-time person detection and people count recording. This essay also discusses several techniques in relation to their applications. With services that have medium to lengthy waiting periods, the proposed method seeks to reduce customer discontent. Keyword : Adaptive random early detection, CNN image processing, cubic function, differential evolution algorithm, dynamic triple exponential smoothing model, System for Managing Hospitals.

Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth

Demand forecasting plays a key role in supply chain planning, management and its sustainable development, but it is a challenging process as demand depends on numerous, often unidentified or unknown factors that are seasonal in nature. Another problem is limited availability of information. Specifically, companies lacking modern IT systems are constrained to rely on historical sales observation as their sole source of information. This paper employs and contrasts a selection of mathematical models for short-term demand forecasting for products whose sales are characterized by high seasonal variations and a development trend. The aim of this publication is to demonstrate that even when only limited empirical data is available, while other factors influencing demand are unknown, it is possible to identify a time series that describes the sales of a product characterized by strong seasonal fluctuations and a trend, using selected mathematical methods. This study uses the seasonal ARIMA (autoregressive integrated moving average) model, ARIMA with Fourier terms model, ETS (exponential smoothing) model, and TBATS (Trigonometric Exponential Smoothing State Space Model with Box–Cox transformation, ARMA errors, Trend and Seasonal component). The models are presented as an alternative to popular machine learning models, which are more complicated to interpret, while their effectiveness is often similar. The selected methods were presented using a case study. The results obtained were compared and the best solution was identified, while emphasizing that each of the methods used could improve demand forecasting in the supply chain.

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

Prediction of COVID-19 Pandemic Trend by Machine Learning

Since 2020, COVID-19 has had a huge impact on people's lives. Including but not limited to economic, educational, medical, and other aspects. During this period, all sectors of society and the government have intervened reasonably, so it is necessary to analyze the data on COVID-19 so far and make scientific predictions. This article starts with the analysis of raw data on COVID-19 from the World Health Organization (WHO). Then four machine learning methods, including the time series model, exponential smoothing model, XGBRegressor method, and polynomial regression model, are leveraged for trend prediction of the original data. The data, with the time ranging from January 2020 to May 2021, is taken as the training object, and then the epidemic in Jul 2021 is used for testing. The number of cases is predicted and compared with real data. It is concluded that the WHO has indeed carried out effective intervention in areas seriously affected by the epidemic and that the time series model predicts the minimum loss value.

Short-Term Insurance Claims Payments Forecasting with Holt-Winter Filtering and Residual Analysis

Time series are essential for anticipating various claims payment applications. For insurance firms to prevent significant losses brought on by potential future claims, the future values of predicted claims are crucial. Additionally, the ideal parameter is chosen artificially. By using a genuine application, the proposed model’s utility is demonstrated. Additionally, the ideal parameter is chosen artificially. By using a genuine application, the proposed model's utility is demonstrated. Also, the single exponential smoothing model is used for prediction under the Holt-Winters’ additive algorithm.

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Abstract Scope and purpose of work This paper examines the impact of the number of gaps in data, the analytical form, and the model type selection criterion on the accuracy of interpolation and extrapolation forecasts for hourly data. Materials and methods Forecasts were developed on the basis of predictors that are based on: classical time series forecasting models and regression time series forecasting models, hybrid time series forecasting models and hybrid regression forecasting models for uncleared series, and exponential smoothing models for cleared series of two or three types of seasonal fluctuations, with minimum estimates of errors in interpolation or extrapolation forecasts. Results Adaptive and hybrid regression models have proved to have the most favorable predictive properties. Most hybrid time series models for systematic and non-systematic gaps and for both analytical forms are single models that generally describe fluctuations within a 24-hour cycle. Conclusions The lowest estimators of prediction errors involving interpolation were obtained for exponential smoothing models, followed by hybrid regression models. A reverse sequence was obtained for extrapolative forecasting.

Forecasting Offline Retail Sales in the COVID-19 Pandemic Period: A Case Study of a Complex Shopping Mall in South Korea

This study examines the case of a shopping mall in Seoul, South Korea, based on its offline retail sales data during the period of the enforcement of the COVID-19 pandemic social distancing policy. South Korea implemented strict social distancing, especially in retail categories where people eat out, due to the danger of spreading infectious disease. A total of 55 retail shops’ sales data were analyzed and classified into five categories: fashion, food and beverage (f&b), entertainment, cosmetics and sport. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were employed, and the autocorrelation (ACF) and partial autocorrelation (PACF) of each retail category’s sales data were analyzed. The mean absolute percentage error (MAPE) was used to determine the most suitable forecasting model for each retail category. In this way, the f&b and entertainment retail categories, in which people eat out, were found to have been significantly impacted, with their 2022 sales forecasted to be less than 80% of their 2018 and 2019 sales. The fashion retail category was also significantly impacted, slowly recovering sales in 2022. The cosmetics and sport retail categories were little impacted by the COVID-19 outbreak, with their retail sales having already recovered by 2022.

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 Waste Mobile Phone (WMP) Quantity and Evaluating the Potential Contribution to the Circular Economy: A Case Study of Turkey

Information and communication technology (ICT)-based products have a significant effect on increasing levels of waste electrical and electronic equipment (WEEE) or electronic waste (e-waste) due to their shorter lifespan as a result of rapid technological changes. Mobile phones are the most popular ICT products, and their market share is increasing gradually. Therefore, effective management of waste mobile phones (WMP) is sought as their recovery brings enormous economic and regulatory benefits. Forecasting the quantities of WMP and their recoverable material content generates valuable data for the related stakeholders in the circular economy (CE) in the design and management of their supply chain networks. This paper presents an approach to determining the WMP quantity for Turkey considering the system from sales to end-of-life (EOL) stages and the years between 2001 and 2035. The proposed model includes two main parts: estimation and forecasting. Firstly, the generated WMP quantity is estimated based on dynamic lifespan and sales using the Distribution Delay (DD) Method considering the years from 2001 to 2020. To select the most suitable model for future projection, seven different time series methods (e.g., Simple Exponential Smoothing, Holt’s, Logistics, Gompertz, Logarithmic, Bass, and ARIMA models) are considered to estimate the generated WMP. For the given data, the Holt’s Method is determined to be the best method to forecast the WMP quantities for the years from 2021 to 2035. In addition, waste materials amount and revenue potentials are estimated for the years from 2001 to 2035. The WMP for Turkey is expected to be approximately 11.5 million units and has a 52 million US$ revenue potential in 2035. The present study contributes to the literature, as it is the first holistic forecasting study on the quantification of WMPs in Turkey. Moreover, since WMPs include remarkable recovery potential in terms of CE, the data and findings of this study may help policymakers, governments, producers, consumers, and all stakeholders to establish effective e-waste management approaches.

Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach.

This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction.

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

AbstractThis paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale‐dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three‐performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.

A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting

The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m−3). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m−3. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m−3, RMSE 1.04 € m−3). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m−3, RMSE 8.09 € m−3), pine (MAE 2.21 € m−3, RMSE 2.83 € m−3), beech (MAE 2.31 € m−3, RMSE 2.70 € m−3), alder (MAE 1.88 € m−3, RMSE 2.40 € m−3), and spruce (MAE 2.44 € m−3, RMSE 2.58 € m−3), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.

Pengurangan Bullwhip Effect pada Rantai Pasok Air Minum Dalam Kemasan Menggunakan Metode Collaborative Planning, Forecasting, and Replenishment (CPFR): Studi Kasus

CV Tirta Alam Dodu is a privately owned company engaged in the beverage industry in the form of AMDK. The problem faced by the company is that there is a difference between demand and sales, or better known as the bullwhip effect problem. The purpose of this study is to reduce the bullwhip effect on the supply chain and optimize the amount of product inventory according to the amount of demand. The method used is the Collaborative Planning, Forecasting, and Replenishment (CPFR) method. Based on the results of the research conducted, it can be concluded that the analysis of demand forecasting calculations on AMDK products at CV Tirta Alam Dodu was carried out using historical data on product demand through five analytical methods, including Linear Trend, Moving Average, Exponential Smoothing, Decomposition, and Winter's Model. The best forecasting resulted in Winter's Model which showed the smallest error value compared to the other four models. The way to minimize so that it does not become a bullwhip effect is to use the CPFR method, where the bullwhip effect value is obtained on glass mineral water products and gallons of mineral water of 0,985 and 0,898, respectively. This result shows that there is no amplification of demand and has reached the company's target, which is to fall below number one. The way to optimize inventory so that there is no excess stock or shortage of stock in the product is to make the amount of inventory based on the amount of demand from the forecasting results plus the safety stock.

Time series modeling and forecasting of vegetable production in United Arab Emirates

Crop forecasting is a formidable challenge. The national and state governments need such predictions before harvesting for various policy decisions relating to storage, distribution, pricing, marketing, importexport etc. In this paper, univariate forecasting models such as random walk, linear trend, quadratic trend, exponential trend, S-curve trend, simple exponential smoothing, Holt's linear exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are used to predict vegetable production in the United Arab Emirates. For empirical analysis, a set of 9 different vegetable groups have been considered, contingent upon availability of required data. Annual data from 1974–75 to 2018–19 was used to forecast the next five years since 2019. Suitable models were selected based on the lowest RMSE and minimum of AIC criterion. Model diagnostic checking was done through Runs above and below the median, Runs up and down and Ljung-Box tests on ACF and PACF of residual terms. For onions and green shallots linear trend model was selected as the best fit, whereas simple exponential smoothing model was most suitable in cauliflowers and broccoli, pumpkins, squash and gourds and spinach. The optimum model obtained for forecasting carrots and turnips was Holt's linear exponential smoothing model and ARIMA model was the best fit for the rest of vegetable groups.

Study on Vegetable Pricing Based on Time Series and Exponential Smoothing Model

Study on Vegetable Pricing Based on Time Series and Exponential Smoothing Model

Анализ долговременных тенденций изменения пассажиропотока в межмуниципальном автобусном сообщении

Transport infrastructure planning and transport policy decisions are important in the issue of regional governance. Bus transportation service is non-alternative for many destinations, therefore it requires understanding of future trends in order to make more grounded decisions. The intermunicipal bus transportation market depends on the general mobility of the population and personal preferences of passengers. Transport behavior is a dynamic characteristic which is influenced by a variety factors. In the long term, it is affected by socio-economic indicators, offers of competing modes of transport, etc. This article analyzes various factors which have an impact on intermunicipal passenger traffic in the Sverdlovsk region. In addition to socio-demographic factors, a change in the transport behavior of the population caused by the sanitary and epidemiological situation and development of the sharing economy, namely, the spread of travel companion search services, is considered. Several scenarios of long-term changes in passenger traffic trends based on causal analysis and mathematical modeling of time series are proposed. In particular, an additive model based on a component-by-component decomposition of a time series and a model of triple exponential smoothing by Holt - Winters are applied.

Best selected forecasting models for COVID-19 pandemic

Abstract This study sought to identify the most accurate forecasting models for COVID-19-confirmed cases, deaths, and recovered patients in Pakistan. For COVID-19, time series data are available from 16 April to 15 August 2021 from the Ministry of National Health Services Regulation and Coordination’s health advice portal. Descriptive as well as time series models, autoregressive integrated moving average, exponential smoothing models (Brown, Holt, and Winters), neural networks, and Error, Trend, Seasonal (ETS) models were applied. The analysis was carried out using the R coding language. The descriptive analysis shows that the average number of confirmed cases, COVID-19-related deaths, and recovered patients reported each day were 2,916, 69.43, and 2,772, respectively. The highest number of COVID-19 confirmed cases and fatalities per day, however, were recorded on April 17, 2021 and April 27, 2021, respectively. ETS (M, N, M), neural network, nonlinear autoregressive (NNAR) (3, 1, 2), and NNAR (8, 1, 4) forecasting models were found to be the best among all other competing models for the reported confirmed cases, deaths, and recovered patients, respectively. COVID-19-confirmed outbreaks, deaths, and recovered patients were predicted to rise on average by around 0.75, 5.08, and 19.11% daily. These statistical results will serve as a guide for disease management and control.

Predicting daily emergency department visits using machine learning could increase accuracy

ObjectiveAdministrators and clinicians alike have attempted to predict emergency department visits for many years. The ability to predict or “forecast” ED visit volume can allow for more efficient resource allocation, including up-staffing or down-staffing, changing OR schedules, and predicting the need for significant resources. The goal of this study is to examine combinations of variables via machine learning to increase prediction accuracy and determine the factors that are most predictive of overall ED visits. As compared to a simple univariate time series model, we hypothesize that machine learning models will predict St. Joseph Mercy Ann Arbor's patient visit load for the emergency department (ED) with higher accuracy than a simple univariate time series model. MethodsUnivariate time series models for daily ED visits, including ARIMA, Exponential Smoothing (ETS), and Facebook Inc.'s prophet algorithm were estimated as a baseline comparison. Machine learning models, including random forests and gradient boosted machines (GBM), were trained using data from 2017 to 2018. After final models were created, they were applied to the 2019 data to determine how well these models predicted actual ED patient volumes in data not utilized during the model fitting process. The accuracy of the machine learning and time series models were assessed based on out-of-sample predictive accuracy, compared using root mean squared error (RMSE). ResultsUsing root mean squared error (RMSE) to assess out-of-sample predictive accuracy of the models, the results showed that the random forest model was the most accurate at predicting daily ED visits in the 2019 test set, followed by the GBM model. These performed only slightly better than the simple exponential smoothing model predictions. The ARIMA model performed poorly in comparison. The day of the week (likely capturing differences between weekdays and weekends) was found to be the most important predictor of patient volumes. Weather-related features such as maximum temperature and SFC pressure appeared to capture some of the seasonality trends related to changes in patient volumes. ConclusionsMachine learning models perform better at predicting daily patient volumes as compared to simple univariate time series models, though not by a substantial amount. Further research can help confirm these limited initial results. Gathering more training data and additional feature engineering could also be beneficial to training the models and potentially improving predictive accuracy.

Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.