Service Demand Prediction with Incomplete Historical Data

2019 ◽  
Author(s):  
Shiheng Ma ◽  
Song Guo ◽  
Kun Wang ◽  
Minyi Guo
Keyword(s):  
Historical Data ◽  
Service Demand ◽  
Demand Prediction

scholarly journals Forecasting telecommunication new service demand by analogy method and combined forecast

2005 ◽  
Vol 15 (1) ◽  
pp. 97-107 ◽  
Author(s):  
Feng-Jenq Lin
Keyword(s):  
Planning Process ◽  
Historical Data ◽  
Optimal Investment ◽  
Telecommunication Industry ◽  
Market Demand ◽  
Analogy Method ◽  
Service Demand ◽  
Forecast Field

In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

scholarly journals An Application of Predictive Analytics in Manufacturing Sector for Price Prediction and Demand Prediction

2020 ◽  
Vol 9 (9) ◽  
pp. 196-199
Keyword(s):  
Machine Learning ◽  
Historical Data ◽  
Predictive Analytics ◽  
Manufacturing Sector ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Demand Prediction ◽  
Price Prediction ◽  
Near Future ◽  
Overall Functioning

Predictive analytics is the examination of concerned data so that we can recognize the problem that may arise in the near future. Manufacturers are interested in quality control, and making sure that the whole factory is functioning at the best possible efficiency. Hence, it’s feasible to increase manufacturing quality, and expect needs throughout the factory with predictive analytics. Hence, we have proposed an application of predictive analytics in manufacturing sector especially focused on price prediction and demand prediction of various products that get manufactured on regular basis. We have trained and tested different machine learning algorithms that can be used to predict price as well as demand of a particular product using historical data about that product’s sales and other transactions. Out of these different tested algorithms, we have selected the regression tree algorithm which gives accuracy of 95.66% for demand prediction and 88.85% for price prediction. Therefore, Regression Tree is best suited for use in manufacturing sector as long as price prediction and demand prediction of a product is concerned. Thus, the proposed application can help the manufacturing sector to improve its overall functioning and efficiency using the price prediction and demand prediction of products.

scholarly journals A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System

2021 ◽  
Vol 13 (17) ◽  
pp. 9692
Author(s):  
Xiaoqing Dai ◽  
Han Qiu ◽  
Lijun Sun
Keyword(s):  
Historical Data ◽  
Urban Rail Transit ◽  
Mathematical Representation ◽  
Natural State ◽  
Rail Transit ◽  
Transit System ◽  
Transit Systems ◽  
Efficient Approach ◽  
Demand Prediction ◽  
Urban Rail

Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. However, data under disruptions are hard to collect due to various reasons, which makes traditional methods impractical in evacuation demand prediction. To address this problem from the modeling perspective, we develop a data-efficient approach to predict evacuation demand for urban rail transit systems under disruptions. Our model-based approach mainly uses historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand for every type of urban rail transit station. Input variables in this step are location features related to the station under the disruption, as well as an origin–destination matrix under the natural state. Then, based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of evacuation demand within the whole network under disruptions. The transport capacity drop under disruptions is used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to implement the proposed method. The results show that our method is able to predict the generation and dissipation processes of evacuation demand, as well model how severely stations will be affected by given disruptions. One general observation we draw from the results is that the most vulnerable stations under disruption, where the locations peak evacuation demand occurs, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the urban rail transit system disruption.

Energy Demand Prediction of the Building Sector Based on Induced Kernel Method and MESSAGEix Model

2019 ◽  
Vol 07 (04) ◽  
pp. 1950016
Author(s):  
Xin TAN ◽  
Zijian ZHAO ◽  
Changyi LIU ◽  
Shining ZHANG ◽  
Xing CHEN ◽  
...  
Keyword(s):  
Energy Demand ◽  
Historical Data ◽  
Kernel Method ◽  
Prediction Methods ◽  
Building Sector ◽  
Related Research ◽  
Energy Prediction ◽  
Demand Prediction ◽  
Final Energy ◽  
Energy Demand Prediction

The building sector, including resident, commercial and public services, is one of the most energy-intensive sectors nowadays. The share of buildings’ energy consumption in the final energy dramatically increases in various scenarios. As the preliminary work of the final energy prediction, the prediction of useful energy demand of the building sector is essential in the fields of energy-related research, especially for the scenarios design. To this end, this paper presents the prediction of energy demand in the building sector based on the Induced Kernel Method (IKM) for the useful energy. First, similar to other learning-based prediction methods, a database is constructed for the training. Specifically, the database contains not only the historical data of the useful energy demand and related indicators, but also some development templates to induce the prediction. Second, the detailed process is mathematically deduced to predict the useful energy demand components of the building sector, including electricity and heating. Finally, using various countries as examples, prediction results of the useful energy are presented in the numerical analysis. Furthermore, by using useful energy prediction results as the input of the MESSAGEix model, the paper further predicts global final energy of the building sector.

Fiscal rules: Excessive limitation or indispensable instrument of fiscal sustainability?

2016 ◽  
pp. 5-29 ◽  
Author(s):  
E. Gurvich ◽  
I. Sokolov
Keyword(s):  
Historical Data ◽  
Federal Budget ◽  
Fiscal Sustainability ◽  
Fiscal Rules ◽  
External Shocks ◽  
Base Price ◽  
Depth Analysis ◽  
Fiscal Balance ◽  
Lower Size ◽  
Budget Expenditure

In-depth analysis of international and Russia’s experiences with implementing fiscal rules is presented. Theoretical and empirical evidences are suggested in favor of retaining the present fiscal rules with some modifications aimed at ensuring: a) a relatively stable level of federal budget expenditure with guaranteed full execution of all commitments; b) countercyclical fiscal policy, based on flexibleand proper reaction to revenue changes; and c) robustness of fiscal rules to internal and external shocks. The main new features suggested include modified calculation of the oil base price, different measurement of cyclical fiscal revenues, lower size of structural fiscal balance, and thorough specification of sources for each item of the balance. The modified rules envisage increased flexibility by relaxing to a pre-set extent and for a pre-set time spending limits in response to extreme shocks. The suggested version of fiscal rules has been tested by application to historical data for 2005-2015, and macro projections for 2015-2025.

Teaching Computer Based Historical Data Analysis

1994 ◽  
Vol 6 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Charles Anderson ◽  
Robert J. Morris
Keyword(s):  
Data Analysis ◽  
Social History ◽  
Historical Data ◽  
Good Practice ◽  
Computer Assisted ◽  
Historical Data Analysis ◽  
University Of Edinburgh ◽  
Computer Based ◽  
The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

A Case Study on the Un-Used Capacity Assessment Using Time Driven Activity Based Costing for Magnetic Components

2020 ◽  
Vol 6 (1) ◽  
pp. 18-39
Author(s):  
Areena Zaini ◽  
Haryantie Kamil ◽  
Mohd Yazid Abu
Keyword(s):  
Historical Data ◽  
Manufacturing Sector ◽  
Type I ◽  
Activity Based Costing ◽  
Cost Rate ◽  
Continuous Innovation ◽  
Magnetic Components ◽  
Alignment Process ◽  
Capacity Cost

The Electrical & Electronic (E&E) company is one of Malaysia’s leading industries that has 24.5% in manufacturing sector production. With a continuous innovation of E&E company, the current costing being used is hardly to access the complete activities with variations required for each workstation to measure the un-used capacity in term of resources and cost. The objective of this work is to develop a new costing structure using time-driven activity-based costing (TDABC) at . This data collection was obtained at E&E company located at Kuantan, Pahang that focusing on magnetic component. The historical data was considered in 2018. TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. This work found three conditions of un-used capacity. Type I is pessimistic situation whereby according to winding toroid core, the un-used capacity of time and cost are -14820 hours and -MYR2.60 respectively. It means the system must sacrifice the time and cost more than actual apportionment. Type II is most likely situation whereby according to assembly process, the un-used capacity of time and cost are 7400 hours and MYR201575.45 respectively. It means the system minimize the time and cost which close to fully utilize from the actual apportionment. Type III is optimistic situation whereby according to alignment process, the un-used capacity of time and cost are 4120 hours and MYR289217.15 respectively. It means the system used small amount of cost and time from the actual apportionment.

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