scholarly journals SALES TIME SERIES ANALYTICS USING DEEP Q-LEARNING

2020 ◽  
pp. 434-441
Author(s):  
Bohdan M. Pavlyshenko
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Cold Start ◽  
Business Environment ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Environment Modeling ◽  
Q Learning ◽  
Learning Agent

The article describes the use of deep Q-learning models in the problems of sales time series analytics. In contrast to supervised machine learning, which is a kind of passive learning, where historical data are used, Q-learning is a kind of active learning aimed at maximizing a reward by optimal sequence of actions. Model free Q-learning approach to optimal pricing strategies and supply-demand problems is considered in the work. The main idea of the study is to show that using deep Q-learning approach in time series analytics causes the sequence of actions to be optimized by maximizing the reward function when the environment for learning agent interaction can be modeled using the parametric model and in the case of using the model which is based on the historical data. In the pricing optimizing case study environment was modeled using sales dependence on extras price and randomly simulated demand. In the pricing optimizing case study, the environment was modeled using sales dependence on extra price and randomly simulated demand. In the supply-demand case study, it was proposed to use historical demand time series for environment modeling, agent states were represented by promo actions, previous demand values and weekly seasonality features. Obtained results show that using deep Q-learning, we can optimize the decision making process for price optimization and supply-demand problems. Environment modeling using parametric models and historical data can be used for the cold start of learning agent. On the next steps, after the cold start, the trained agent can be used in real business environment.

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

2020 ◽  
Vol 13 (3) ◽  
pp. 915-927 ◽  
Author(s):  
Dostdar Hussain ◽  
Tahir Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
River Basin ◽  
Time Series Prediction ◽  
Learning Approach

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 226
Author(s):  
Marek Hermansa ◽  
Michał Kozielski ◽  
Marcin Michalak ◽  
Krzysztof Szczyrba ◽  
Łukasz Wróbel ◽  
...  
Keyword(s):  
Historical Data ◽  
Cold Start ◽  
Predictive Maintenance ◽  
Use Cases ◽  
False Alarms ◽  
Data Sets ◽  
Outlier Identification ◽  
Machine Operator ◽  
System Operating

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

scholarly journals The Impact of the Business Environment in Developing an Enterprise - Case Study: Furniture Manufacturing Tefik Canga Design

2015 ◽  
Vol 5 (2) ◽  
pp. 93
Author(s):  
MSc. Arbenita Topalli
Keyword(s):  
Historical Data ◽  
Business Environment ◽  
Production Costs ◽  
Market Segment ◽  
Competitive Pressure ◽  
Input Output ◽  
Furniture Manufacturing ◽  
Productivity Increase ◽  
The Impact

The enterprise is an organization that reconciles the workforce, real capital, techniques, information and knowledge to produce and service. The enterprise cannot control or have any impact on the environment that it`s surrounded by, only to adjust to it. Despite that, enterprises can be specialized in a special market segment, yet it will be subject to competitive pressure. Furniture Manufacturing Tefik Canga Design has taken over the use of natural and unnatural resources to promote the development and growth, or to solve financial and operational problems. With this research, we propose a growth model of a manufacturing enterprise in relation to the business environment. Implementation of the case is described and the profitability and productivity performance is analyzed using five years of historical data product. Therefore, by this study is expected to achieve results, that will improve the input-output relationship in order to increase the firm's productivity, increase the value of employees and create opportunities for development in order to cut production costs.

scholarly journals A method to estimate the demand for freight transport in absence of historical data. A case study of the Oder Waterway

2018 ◽  
Vol 58 ◽  
pp. 01009
Author(s):  
Ludmiła Filina-Dawidowicz ◽  
Izabela Kotowska ◽  
Marta Mańkowska ◽  
Michał Pluciński
Keyword(s):  
Time Series ◽  
Qualitative Methods ◽  
Time Series Data ◽  
Historical Data ◽  
Freight Transport ◽  
Series Data ◽  
Class Iii ◽  
Research Results

The aim of the research described in this article is to work out a method to estimate the demand for freight transport in a situation when no historical data are available, thus rendering it impossible to apply methods based on time series data. The method presented in this article was developed and verified on the basis of an analysis of potential inland shipping operations on the Oder Waterway to/from the seaports in Szczecin and Świnoujście, assuming that the waterway has been upgraded to navigability class III. The analysis was predicated on a demand survey performed among cargo shippers. The obtained research results made it possible to specify the advantages and drawbacks of forecasting based on qualitative methods, and to identify the factors which significantly reduce the reliability of freight transport forecasts.

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.

Participation and spatial transformation in large housing estates in Poland. Case study of Ursynów Północny

2018 ◽  
Vol 60 (1) ◽  
pp. 55-65
Author(s):  
Krystyna Ilmurzyńska
Keyword(s):  
Basic Assumption ◽  
Real Life ◽  
Business Environment ◽  
Spatial Transformation ◽  
Reciprocal Relationships ◽  
Spatial Transformations ◽  
Large Housing Estates ◽  
Existing Housing ◽  
Housing Estates

Abstract This article investigates the suitability of traditional and participatory planning approaches in managing the process of spatial development of existing housing estates, based on the case study of Warsaw’s Ursynów Północny district. The basic assumption of the article is that due to lack of government schemes targeted at the restructuring of large housing estates, it is the business environment that drives spatial transformations and through that shapes the development of participation. Consequently the article focuses on the reciprocal relationships between spatial transformations and participatory practices. Analysis of Ursynów Północny against the background of other estates indicates that it presents more endangered qualities than issues to be tackled. Therefore the article focuses on the potential of the housing estate and good practices which can be tracked throughout its lifetime. The paper focuses furthermore on real-life processes, addressing the issue of privatisation, development pressure, formal planning procedures and participatory budgeting. In the conclusion it attempts to interpret the existing spatial structure of the estate as a potential framework for a participatory approach.

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