scholarly journals A Reservation Aggregation Framework Design for Demand Estimation

10.14311/860 ◽  
2006 ◽  
Vol 46 (4) ◽  
Author(s):  
T. Vítek ◽  
D. Pachner
Keyword(s):  
Time Series ◽  
Management Practices ◽  
Linear Models ◽  
Process Time ◽  
Hotel Management ◽  
Control Engineering ◽  
Demand Prediction ◽  
Pricing Decisions ◽  
Framework Design ◽  
Market Search

Effective management practices in the tourism and hotel area have seldom been more important than at the present time. Pricing decisions cannot be taken without serious thought. IT has provided the opportunity for a customer to make a quick market search and it offers decision support systems that can be used in the hotel management. The heart of yield management system consists of the predicting machine, which estimates the number of incoming reservations. Incoming reservations arrive randomly in time. The time series calculi as well as the estimators known from control engineering require properly defined time rows (with a constant period). This requirement is usually not fulfilled, so the input data are not exploited properly. This paper outlines a procedure that aggregates the reservations into a time series that is useful for demand prediction. The algorithm prepares the data systematically for further processing. Any method that process time rows can be used for subsequent prediction: time series, linear models or time extrapolation. 

Analysis and prediction of groundwater level time series with Autoregressive Linear Models

2016 ◽  
Vol 39 ◽  
pp. 109-112
Author(s):  
Mirko Ginocchi ◽  
Giovanni Franco Crosta ◽  
Marco Rotiroti ◽  
Tullia Bonomi
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Linear Models

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals STATE ANALYSIS INTERNET OF THINGS DEVICES BASED ON BAGGING

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 45-50
Author(s):  
Mikhail E. Sukhoparov ◽  
◽  
Ilya S. Lebedev ◽  
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
The State ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Time ◽  
K Nearest Neighbors ◽  
Computational Point ◽  
Iot Devices ◽  
The Internet Of Things

The development of IoT concept makes it necessary to search and improve models and methods for analyzing the state of remote autonomous devices. Due to the fact that some devices are located outside the controlled area, it becomes necessary to develop universal models and methods for identifying the state of low-power devices from a computational point of view, using complex approaches to analyzing data coming from various information channels. The article discusses an approach to identifying IoT devices state, based on parallel functioning classifiers that process time series received from elements in various states and modes of operation. The aim of the work is to develop an approach for identifying the state of IoT devices based on time series recorded during the execution of various processes. The proposed solution is based on methods of parallel classification and statistical analysis, requires an initial labeled sample. The use of several classifiers that give an answer "independently" from each other makes it possible to average the error by "collective" voting. The developed approach is tested on a sequence of classifying algorithms, to the input of which the time series obtained experimentally under various operating conditions were fed. Results are presented for a naive Bayesian classifier, decision trees, discriminant analysis, and the k nearest neighbors method. The use of a sequence of classification algorithms operating in parallel allows scaling by adding new classifiers without losing processing speed. The method makes it possible to identify the state of the Internet of Things device with relatively small requirements for computing resources, ease of implementation, and scalability by adding new classifying algorithms.

scholarly journals Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

2021 ◽  
Vol 13 (19) ◽  
pp. 3994
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Sisi Wei ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Development ◽  
Large Scale ◽  
Management Practices ◽  
Conditional Random Field ◽  
Paddy Rice ◽  
Growth Patterns ◽  
Selection Strategy ◽  
Svm Classifier ◽  
Humid Climate

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.

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