Dynamic Properties of Quantity Adjustment Process Under Demand Forecast Formed by Moving Average of Past Demands

2019 ◽  
pp. 195-255
Author(s):  
Yoshinori Shiozawa ◽  
Masashi Morioka ◽  
Kazuhisa Taniguchi
Keyword(s):  
Dynamic Properties ◽  
Moving Average ◽  
Adjustment Process ◽  
Demand Forecast ◽  
Quantity Adjustment

Optimization of Forecasting Moving Average Error in Probabilistic Demand Using Genetic Algorithm Based Fuzzy Logic

2012 ◽  
Vol 576 ◽  
pp. 710-713
Author(s):  
Chairul Saleh ◽  
Muhammad Ridwan Andi Purnomo ◽  
Hayati Mukti Asih
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Moving Average ◽  
Demand Forecasting ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Demand Forecast ◽  
Advantages And Disadvantages ◽  
Excess Production ◽  
Before And After

Demand forecasting is one of the most critical factors in production planning. The uncertain demand, which is the basic idea of planning the production level, nowadays is one of serious problems. The inaccurate demand forecasting could affect to excess production or shortage stocks which led to lost sales. Usually, the forecasted result is hard to represent real condition. Some studies already conducted related to fuzzy time series, each of them has its own advantages and disadvantages compared to other approaches. This research presents the combination of simple moving average forecasting and fuzzy logic model to demand forecast. Then, genetic algorithm (GA) is applied to optimize the mean square error (MSE) inside the fuzzy system. The MSE before and after GA optimization is 0,2192 and 0,1821, respectively.

Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models

2014 ◽  
Vol 672-674 ◽  
pp. 2085-2097 ◽  
Author(s):  
Sue Ling Lai ◽  
Ming Liu ◽  
Kuo Cheng Kuo ◽  
Ray Chang
Keyword(s):  
Hong Kong ◽  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Critical Role ◽  
Forecast Accuracy ◽  
Demand Forecast ◽  
Forecast Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

scholarly journals Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

2021 ◽  
Vol 9 (1) ◽  
pp. 73-89
Author(s):  
Guoyou Yue
Keyword(s):  
Moving Average ◽  
Beibu Gulf ◽  
Demand Forecast ◽  
Forecasting Models ◽  
Forecasting Method ◽  
Port Logistics ◽  
Logistics Demand ◽  
Cargo Throughput ◽  
Port Areas ◽  
Weighted Moving Average

Objective - The objective of this paper is to establish the forecasting models of port cargo throughput and container throughput in Guangxi Beibu Gulf Port in the next 5 years, and to put forward the countermeasures of port logistics development in Guangxi Beibu Gulf Port according to the forecast results. Methodology/Technique – The data of cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2009-2020 are collected through the data of Guangxi Statistical Yearbook and Guangxi Statistical Bulletin. Based on 2019 and 2020, the forecasting models of cargo throughput and container throughput in Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou are establishe using a weighted moving average forecasting method. The cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2020/2021-2025 are predicted. Findings – The forecast results show that by 2025, the cargo throughput of Guangxi Beibu Gulf Port is expected to exceed 400 million tons, and the container throughput is expected to exceed 10 million TEU. According to the fitting diagram of forecast results and actual data, it can be seen that the accuracy of the forecast results is very high. Novelty – It is innovative to select 2 base years in 2019 and 2020 to establish forecasting model. Based on the comparative analysis of the forecast results, this paper puts forward various measures to promote the development of port logistics of Guangxi Beibu Gulf port, such as strengthening the construction of port self-condition, strengthening the co-ordinated development of port and economic hinterland, speeding up the construction of port collection and distribution system, training and introducing all kinds of high-quality port logistics talents. Type of Paper: Empirical. JEL Classification: C53, R41. Keywords: Logistics Demand Forecast; Cargo Throughput Forecast; Container Throughput Forecast; Weighted Moving Average Forecasting Method; Guangxi Beibu Gulf Port Reference to this paper should be made as follows: Yue, N. (2021). Forecasting the Logistics Demand of Guangxi Beibu Gulf Port, GATR Global J. Bus. Soc. Sci. Review, 9(1): 73 – 89. https://doi.org/10.35609/gjbssr.2021.9.1(9)

scholarly journals Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

2020 ◽  
Vol 11 (1) ◽  
pp. 153-153
Author(s):  
Guoyou Yue
Keyword(s):  
Moving Average ◽  
Prediction Method ◽  
Western China ◽  
Beibu Gulf ◽  
Demand Forecast ◽  
Healthy Development ◽  
Port Logistics ◽  
Asean Countries ◽  
Logistics Demand ◽  
Cargo Throughput

In February 2007, Guangxi Zhuang Autonomous Region People's Government integrated Fangcheng, Qinzhou and Beihai three coastal ports to establish Guangxi Beibu Gulf International Port Group Co., LTD. At this point, Guangxi Beibu Gulf Port integration of three, Guangxi Beibu Gulf Port ushered in a major development opportunity. Guangxi Beibu Gulf Port is one of the important ports in China's coastal cities open to the outside world. It is the meeting point of the three economic circles of South China, Southwest China and ASEAN, and also the most convenient land and sea passage between China and ASEAN countries. With the vigorous implementation of the "Belt and Road" initiative and the construction of new land and sea passages in western China, Guangxi Beibu Gulf Port has become an important gateway and connection point for the implementation of these strategies. Its strategic status keeps rising, and Guangxi Beibu Gulf Port has also developed rapidly. In recent years, how is the port logistics development of Guangxi Beibu Gulf Port? What are the changing rules and trends of its cargo throughput and container throughput? What measures can continuously and effectively promote the port logistics development of Guangxi Beibu Gulf Port? This paper will carry out research and analysis on these problems in order to better promote the healthy development of Guangxi Beibu Gulf Port. Keywords: Logistics demand forecast, Cargo throughput forecast, Container throughput forecast, Weighted moving average prediction method, Guangxi Beibu Gulf Port

scholarly journals Identification and Estimation in Non-Fundamental Structural VARMA Models

2019 ◽  
Vol 87 (4) ◽  
pp. 1915-1953 ◽  
Author(s):  
Christian Gouriéroux ◽  
Alain Monfort ◽  
Jean-Paul Renne
Keyword(s):  
Dynamic Properties ◽  
Moving Average ◽  
Real Data ◽  
Identification Problem ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Linear Transformations ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Matrix Polynomials

Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is “dynamic” and is a consequence of the fact that, even if a SVARMA admits a non-invertible moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.

scholarly journals A Comparison of Bullwhip Effect under Various Forecasting Techniques in Supply Chains with Two Retailers

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Junhai Ma ◽  
Xiaogang Ma
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Market Competition ◽  
Bullwhip Effect ◽  
Demand Forecast ◽  
Autoregressive Moving Average Model ◽  
Minimum Mean Squared Error ◽  
Forecasting Methods ◽  
The Impact ◽  
Forecasting Techniques

We examine the impact of three forecasting methods on the bullwhip effect in a two-stage supply chain with one supplier and two retailers. A first order mixed autoregressive-moving average model (ARMA(1, 1)) performs the demand forecast and an order-up-to inventory policy characterizes the inventory decision. The bullwhip effect is measured, respectively, under the minimum mean-squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting techniques. The effect of parameters on the bullwhip effect under three forecasting methods is analyzed and the bullwhip effect under three forecasting methods is compared. Conclusions indicate that different forecasting methods lead to different bullwhip effects caused by lead time, underlying parameters of the demand process, market competition, and the consistency of demand volatility between two retailers. Moreover, some suggestions are present to help managers to select the forecasting method that yields the lowest bullwhip effect.

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