Search results for: cost forecasting
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 6285

Search results for: cost forecasting

6285 A New Model for Production Forecasting in ERP

Authors: S. F. Wong, W. I. Ho, B. Lin, Q. Huang

Abstract:

ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting.

Keywords: ERP, grey system, LSSVM, production forecasting

Procedia PDF Downloads 409
6284 Improving Forecasting Demand for Maintenance Spare Parts: Case Study

Authors: Abdulaziz Afandi

Abstract:

Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

Procedia PDF Downloads 85
6283 Electricity Demand Modeling and Forecasting in Singapore

Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

Abstract:

In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

Procedia PDF Downloads 600
6282 Load Forecasting in Short-Term Including Meteorological Variables for Balearic Islands Paper

Authors: Carolina Senabre, Sergio Valero, Miguel Lopez, Antonio Gabaldon

Abstract:

This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research.

Keywords: short-term load forecasting, power demand, neural networks, load forecasting

Procedia PDF Downloads 144
6281 Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships, PSO Technique, and Automatic Clustering Algorithm

Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

Abstract:

Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

Procedia PDF Downloads 212
6280 Collaborative Planning and Forecasting

Authors: Neha Asthana, Vishal Krishna Prasad

Abstract:

Collaborative planning and forecasting are the innovative and systematic approaches towards productive integration and assimilation of data synergized into information. The changing and variable market dynamics have persuaded global business chains to incorporate collaborative planning and forecasting as an imperative tool. Thus, it is essential for the supply chains to constantly improvise, update its nature, and mould as per changing global environment.

Keywords: information transfer, forecasting, optimization, supply chain management

Procedia PDF Downloads 391
6279 Lee-Carter Mortality Forecasting Method with Dynamic Normal Inverse Gaussian Mortality Index

Authors: Funda Kul, İsmail Gür

Abstract:

Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions.

Keywords: mortality, forecasting, lee-carter model, normal inverse gaussian distribution

Procedia PDF Downloads 318
6278 Utilizing Grid Computing to Enhance Power Systems Performance

Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

Abstract:

Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

Procedia PDF Downloads 435
6277 Cost Overruns in Mega Projects: Project Progress Prediction with Probabilistic Methods

Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

Abstract:

Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

Procedia PDF Downloads 350
6276 Forecasting Amman Stock Market Data Using a Hybrid Method

Authors: Ahmad Awajan, Sadam Al Wadi

Abstract:

In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

Procedia PDF Downloads 88
6275 Comparison of Applicability of Time Series Forecasting Models VAR, ARCH and ARMA in Management Science: Study Based on Empirical Analysis of Time Series Techniques

Authors: Muhammad Tariq, Hammad Tahir, Fawwad Mahmood Butt

Abstract:

Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model.

Keywords: forecasting, time series, auto regression, ARCH, ARMA

Procedia PDF Downloads 289
6274 pscmsForecasting: A Python Web Service for Time Series Forecasting

Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

Abstract:

pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application.

Keywords: time series, forecasting, web service, open source

Procedia PDF Downloads 32
6273 An Analytical Method for Maintenance Cost Estimating Relationships of Helicopters Using Linear Programming

Authors: Meesun Sun, Yongmin Kim

Abstract:

Estimating maintenance cost is crucial in defense management because it affects military budgets and availability of equipment. When it comes to estimating maintenance cost of the deployed equipment, time series forecasting can be applied with the actual historical cost data. It is more difficult issue to estimate maintenance cost of new equipment for which the actual costs are not provided. In this underlying context, this study proposes an analytical method for maintenance cost estimating relationships (CERs) development of helicopters using linear programming. The CERs can be applied to a new helicopter because they use non-cost independent variables such as the number of engines, the empty weight and so on. In the Republic of Korea, the maintenance cost of new equipment has been usually estimated by reflecting maintenance cost to unit price ratio of the legacy equipment. This study confirms that the CERs perform well for the 10 types of airmobile helicopters in terms of mean absolute percentage error by applying leave-one-out cross-validation. The suggested method is very useful to estimate the maintenance cost of new equipment and can help in the affordability assessment of acquisition program portfolios for total life cycle systems management.

Keywords: affordability analysis, cost estimating relationship, helicopter, linear programming, maintenance cost

Procedia PDF Downloads 102
6272 Forecasting Stock Prices Based on the Residual Income Valuation Model: Evidence from a Time-Series Approach

Authors: Chen-Yin Kuo, Yung-Hsin Lee

Abstract:

Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

Procedia PDF Downloads 277
6271 Two Day Ahead Short Term Load Forecasting Neural Network Based

Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

Procedia PDF Downloads 415
6270 Forecasting Future Demand for Energy Efficient Vehicles: A Review of Methodological Approaches

Authors: Dimitrios I. Tselentis, Simon P. Washington

Abstract:

Considerable literature has been focused over the last few decades on forecasting the consumer demand of Energy Efficient Vehicles (EEVs). These methodological issues range from how to capture recent purchase decisions in revealed choice studies and how to set up experiments in stated preference (SP) studies, and choice of analysis method for analyzing such data. This paper reviews the plethora of published studies on the field of forecasting demand of EEVs since 1980, and provides a review and annotated bibliography of that literature as it pertains to this particular demand forecasting problem. This detailed review addresses the literature not only to Transportation studies, but specifically to the problem and methodologies around forecasting to the time horizons of planning studies which may represent 10 to 20 year forecasts. The objectives of the paper are to identify where existing gaps in literature exist and to articulate where promising methodologies might guide longer term forecasting. One of the key findings of this review is that there are many common techniques used both in the field of new product demand forecasting and the field of predicting future demand for EEV. Apart from SP and RP methods, some of these new techniques that have emerged in the literature in the last few decades are survey related approaches, product diffusion models, time-series modelling, computational intelligence models and other holistic approaches.

Keywords: demand forecasting, Energy Efficient Vehicles (EEVs), forecasting methodologies review, methodological approaches

Procedia PDF Downloads 451
6269 Forecasting of Grape Juice Flavor by Using Support Vector Regression

Authors: Ren-Jieh Kuo, Chun-Shou Huang

Abstract:

The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

Procedia PDF Downloads 443
6268 A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis

Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

Abstract:

Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

Procedia PDF Downloads 388
6267 Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh

Authors: S. M. Anowarul Haque, Md. Asiful Islam

Abstract:

Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

Procedia PDF Downloads 135
6266 A Case Study of Typhoon Tracks: Insights from the Interaction between Typhoon Hinnamnor and Ocean Currents in 2022

Authors: Wei-Kuo Soong

Abstract:

The forecasting of typhoon tracks remains a formidable challenge, primarily attributable to the paucity of observational data in the open sea and the intricate influence of weather systems at varying scales. This study investigates the case of Typhoon Hinnamnor in 2022, examining its trajectory and intensity fluctuations in relation to the interaction with a concurrent tropical cyclone and sea surface temperatures (SST). Utilizing the Weather Research and Forecasting Model (WRF), to simulate and analyze the interaction between Typhoon Hinnamnor and its environmental factors, shedding light on the mechanisms driving typhoon development and enhancing forecasting capabilities.

Keywords: typhoon, sea surface temperature, forecasting, WRF

Procedia PDF Downloads 17
6265 Applying Arima Data Mining Techniques to ERP to Generate Sales Demand Forecasting: A Case Study

Authors: Ghaleb Y. Abbasi, Israa Abu Rumman

Abstract:

This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking.

Keywords: ARIMA models, sales demand forecasting, time series, R code

Procedia PDF Downloads 346
6264 Electric Load Forecasting Based on Artificial Neural Network for Iraqi Power System

Authors: Afaneen Anwer, Samara M. Kamil

Abstract:

Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.

Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system

Procedia PDF Downloads 544
6263 A Smart Contract Project: Peer-to-Peer Energy Trading with Price Forecasting in Microgrid

Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

Abstract:

Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

Procedia PDF Downloads 76
6262 Electricity Price Forecasting: A Comparative Analysis with Shallow-ANN and DNN

Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

Procedia PDF Downloads 254
6261 Estimation and Forecasting with a Quantile AR Model for Financial Returns

Authors: Yuzhi Cai

Abstract:

This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

Procedia PDF Downloads 306
6260 Forecasting Unemployment Rate in Selected European Countries Using Smoothing Methods

Authors: Ksenija Dumičić, Anita Čeh Časni, Berislav Žmuk

Abstract:

The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates.

Keywords: European Union countries, exponential smoothing methods, forecast accuracy unemployment rate

Procedia PDF Downloads 336
6259 Energy Consumption Forecast Procedure for an Industrial Facility

Authors: Tatyana Aleksandrovna Barbasova, Lev Sergeevich Kazarinov, Olga Valerevna Kolesnikova, Aleksandra Aleksandrovna Filimonova

Abstract:

We regard forecasting of energy consumption by private production areas of a large industrial facility as well as by the facility itself. As for production areas the forecast is made based on empirical dependencies of the specific energy consumption and the production output. As for the facility itself implementation of the task to minimize the energy consumption forecasting error is based on adjustment of the facility’s actual energy consumption values evaluated with the metering device and the total design energy consumption of separate production areas of the facility. The suggested procedure of optimal energy consumption was tested based on the actual data of core product output and energy consumption by a group of workshops and power plants of the large iron and steel facility. Test results show that implementation of this procedure gives the mean accuracy of energy consumption forecasting for winter 2014 of 0.11% for the group of workshops and 0.137% for the power plants.

Keywords: energy consumption, energy consumption forecasting error, energy efficiency, forecasting accuracy, forecasting

Procedia PDF Downloads 392
6258 Influence of Radio Frequency Identification Technology at Cost of Supply Chain as a Driver for the Generation of Competitive Advantage

Authors: Mona Baniahmadi, Saied Haghanifar

Abstract:

Radio Frequency Identification (RFID) is regarded as a promising technology for the optimization of supply chain processes since it improves manufacturing and retail operations from forecasting demand for planning, managing inventory, and distribution. This study precisely aims at learning to know the RFID technology and at explaining how it can concretely be used for supply chain management and how it can help improving it in the case of Hejrat Company which is located in Iran and works on the distribution of medical drugs and cosmetics. This study uses some statistical analysis to calculate the expected benefits of an integrated RFID system on supply chain obtained through competitive advantages increases with decreasing cost factor. The study investigates how the cost of storage process, labor cost, the cost of missing goods, inventory management optimization, on-time delivery, order cost, lost sales and supply process optimization affect the performance of the integrated RFID supply chain regarding cost factors and provides a competitive advantage.

Keywords: cost, competitive advantage, radio frequency identification, supply chain

Procedia PDF Downloads 234
6257 Estimation and Forecasting Debris Flow Phenomena on the Highway of the 'TRACECA' Corridor

Authors: Levan Tsulukidze

Abstract:

The paper considers debris flow phenomena and forecasting of them in the corridor of ‘TRACECA’ on the example of river Naokhrevistkali, as well as the debris flow -type channel passing between the villages of Vale-2 and Naokhrevi. As a result of expeditionary and reconnaissance investigations, as well as using empiric dependencies, the debris flow expenditure has been estimated in case of different debris flow provisions.

Keywords: debris flow, Traceca corridor, forecasting, river Naokhrevistkali

Procedia PDF Downloads 320
6256 Mixed Effects Models for Short-Term Load Forecasting for the Spanish Regions: Castilla-Leon, Castilla-La Mancha and Andalucia

Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

Abstract:

This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

Procedia PDF Downloads 150