scholarly journals A Data-Weighted Prior Estimator for Forecast Combination

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 429 ◽  
Author(s):  
Esteban Fernández-Vázquez ◽  
Blanca Moreno ◽  
Geoffrey J.D. Hewings
Keyword(s):  
A Priori ◽  
Equal Weight ◽  
Forecast Combination ◽  
Simple Arithmetic ◽  
The Past ◽  
Combination Methods ◽  
Combining Forecasts ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Linear Statistical Models

Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed model with a simulation exercise and compare its ex ante forecasting performance with other methods used to combine forecasts. The obtained results suggest that the proposed method dominates other combining methods, such as equal-weight averages or ordinal least squares methods, among others.

scholarly journals Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 260 ◽  
Author(s):  
Dazhi Yang ◽  
Allan N. Zhang
Keyword(s):  
Supply Chain ◽  
Consumer Goods ◽  
Business Strategy ◽  
Fast Moving ◽  
A Priori ◽  
Forecast Accuracy ◽  
Forecast Combination ◽  
Demand Forecast ◽  
Combination Methods ◽  
Fast Moving Consumer Goods

This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

scholarly journals Forecasting Inflation: A Combination Approach

2020 ◽  
Vol 31 (2) ◽  
pp. 145-154
Author(s):  
Martin Bojaj ◽  
Gordana Djurovic
Keyword(s):  
Weighted Average ◽  
Research Problem ◽  
Inflation Expectations ◽  
Forecast Combination ◽  
The European Union ◽  
Main Research ◽  
Inflation Model ◽  
Combination Methods ◽  
Combining Forecasts ◽  
The Government

The objective of this paper is to investigate and forecast the determinants of Montenegrin inflation empirically, using forecast combination methods, from January 2006 to December 2016, and out-of-sample 12-month horizon forecasting from January 2017 to December 2017. The main research problem is that given the struggle policymakers have had to define proper criteria to diagnose the onset of inflation indicators, we felt compelled to identify an approach and methodology that the government of Montenegro can use in the threshold to accessing the European Union. We examine three individual-predictor SVAR models to forecast inflation.  Model 1 examines the internal determinants of inflation. Model 2 relates to demand-pull and cost-push variables. Model 3 examines external determinants. Combining the above three forecasts, we disclose two more RMSEs: equal and inverse MSE weights. Model 1 predicts inflation at 1.3%, the inverse MSE at 1.5%, and the weighted average at 1.4%. They show forecasting performances that are sustainable and average inflation not more than 1.5% above the rate of the three best performing Member states: Cyprus (0.2%), Ireland (0.3%), and Finland (0.8%) over the 12 months covering April 2017-March 2018. Our findings allow the policymakers to understand the factors involved in identifying the onset of inflation dynamics and inflation expectations in Montenegro better and develop more effective government regulations that can be employed nationally. In so doing, this research advances and recommends the toolset needed, combining forecasts, to combat the concerns of many macroprudential policymakers in Montenegro, especially the Central Bank of Montenegro.

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

Fast Transaction-Level Model for Direct Memory Access Controller

2019 ◽  
Vol 28 (04) ◽  
pp. 1950059
Author(s):  
Mona Safar ◽  
Magdy A. El-Moursy ◽  
Ahmed Tarek ◽  
Ahmed Emad ◽  
Ahmed Hesham ◽  
...  
Keyword(s):  
Open Source ◽  
Direct Memory Access ◽  
System Level ◽  
The Past ◽  
Simulation Speed ◽  
Proposed Model ◽  
Level Model ◽  
Data Bus ◽  
Transaction Level ◽  
Simulation Time

Transaction-Level Modeling (TLM) has been widely used in system-level design in the past few years. Simulation speed of Virtual Platforms (VPs) depends mainly on the transactions which are initiated by the Programmer’s View (PV) models of the VP devices. PV models are required to run at highest simulation speed. Data bus width as a hardware (HW) parameter should not reduce simulation speed of the modeled transactions. Furthermore, HW-related parameters should only be accounted for when considering timing of the models. A fast SystemC-TLM model is developed for the widely used ARM PrimeCell PL080 DMAC IP. The performance of the proposed model is validated against a developed RTL model for the same device. The effect of the transactions granularity on simulation speed is determined. Different programmed transfers are simulated and compared with open-source Quick Emulator (QEMU)-based models. The developed model is compared with the developed RTL, the open-source QEMU model, and the existing ARM Fast Model (AFM). It is shown that simulation time of the developed model is reduced by two orders of magnitude as compared to the other existing models.

scholarly journals Sales Growth Rate Forecasting Using Improved PSO and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xibin Wang ◽  
Junhao Wen ◽  
Shafiq Alam ◽  
Xiang Gao ◽  
Zhuo Jiang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Decisive Role ◽  
Convergence Speed ◽  
Classification Model ◽  
Support Vector ◽  
Local Optimum ◽  
Sales Growth ◽  
Local Optima ◽  
Proposed Model ◽  
Forecasting Performance

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

scholarly journals Presentation a Conceptual Model to Explain Determinants of Breakthrough the Islamic Banking Marketing: A Managerial Perspective

2014 ◽  
Vol 39 ◽  
pp. 58-66
Author(s):  
Seyed Reza Seyed-Javadin ◽  
Reza Raei ◽  
Mohammad Javad Iravani ◽  
Mohammad Safari
Keyword(s):  
Conceptual Model ◽  
Financial Services ◽  
Islamic Banking ◽  
Marketing Capabilities ◽  
Great Opportunity ◽  
The Past ◽  
Proposed Model ◽  
Determining Factors ◽  
Descriptive Method ◽  
Managerial Perspective

Taking advantage of applications of marketing in the Islamic banking is a great opportunity for this area to gain competitive advantage in the today’s turbulent business and market. Specialized field of Islamic banking marketing is a subset of marketing management has received less attention and consideration. Islamic banking (IB) is one of the growing fields in the today's economy. To achieve more advancement in the IB it is necessary that recent findings of the other research and practical areas to be used and implemented. Scholars and experts believe that the market for Islamic banking has grown rapidly over the past few years, and this robust growth is expected to continue for the foreseeable future. In many markets, Islamic banking has evolved from being a niche offering into being part of the mainstream financial services landscape. Marketing capabilities can provide the convenient and required ground for the continued growth of Islamic banking. This study aimed at present a conceptual model to explain the determining factors to achieve the IB marketing from managerial perspective. Using a descriptive method this study tried to identify and present the main factors from managerial perspective that affected on the IB marketing. Proposed model and appropriated explanations have been provided in the paper.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

2021 ◽  
pp. 004728752110612
Author(s):  
Yuying Sun ◽  
Jian Zhang ◽  
Xin Li ◽  
Shouyang Wang
Keyword(s):  
Structural Changes ◽  
Model Averaging ◽  
Structural Instability ◽  
Time Varying ◽  
Forecast Combination ◽  
Tourism Demand ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Methods ◽  
Out Of Sample

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

Data Mining for Combining Forecasts in Inventory Management

2008 ◽  
pp. 2792-2797
Author(s):  
Chi Kin Chan
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Traditional Approach ◽  
The Past ◽  
Aggregate Information ◽  
Forecasting Methods ◽  
Combining Forecasts ◽  
Forecasting Method ◽  
The Future ◽  
Single Method

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts.

Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

2015 ◽  
Vol 6 (4) ◽  
pp. 83-102 ◽  
Author(s):  
Eufemia Tarantino ◽  
Antonio Novelli ◽  
Mariella Aquilino ◽  
Benedetto Figorito ◽  
Umberto Fratino
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Sensor Data ◽  
Network Simulator ◽  
Research Activity ◽  
The Past ◽  
Time Space ◽  
Multi Temporal ◽  
Study Case ◽  
Landsat Satellite

This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

Incremental Knowledge Construction for Real-World Event Understanding

2010 ◽  
Vol 4 (1) ◽  
pp. 65-79 ◽  
Author(s):  
Koji Kamei ◽  
Yutaka Yanagisawa ◽  
Takuya Maekawa ◽  
Yasue Kishino ◽  
Yasushi Sakurai ◽  
...  
Keyword(s):  
Real World ◽  
Knowledge Construction ◽  
A Priori ◽  
Sensor System ◽  
World Knowledge ◽  
Incremental Model ◽  
Proposed Model

The construction of real-world knowledge is required if we are to understand real-world events that occur in a networked sensor environment. Since it is difficult to select suitable ‘events’ for recognition in a sensor environment a priori, we propose an incremental model for constructing real-world knowledge. Labeling is the central plank of the proposed model because the model simultaneously improves both the ontology of real-world events and the implementation of a sensor system based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and is evaluated in a practical labeling experiment.

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