scholarly journals Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry

2020 ◽  
Vol 12 (18) ◽  
pp. 7334
Author(s):  
Tianxiang Zheng ◽  
Shaopeng Liu ◽  
Zini Chen ◽  
Yuhan Qiao ◽  
Rob Law
Keyword(s):  
Time Series ◽  
Hong Kong ◽  
Demand Forecasting ◽  
Distribution Channels ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Data Set ◽  
Hotel Room ◽  
The Impact

Given the influence of the financial-economic crisis, hotel room demand in Hong Kong has experienced a significant drop since June 2019. Given that studies on the room rate aspect remains limited, this study considers the demand for hotel rooms from different categories and districts. This study makes forecast attempts for room rates from mid-October of 2019 to mid-June of 2020, which was a difficult period for Hong Kong owing to the onset of the social unrest and novel coronavirus outbreak. This study develops an approach to the short-term forecasting of hotel daily room rates on the basis of the Long Short-Term Memory (LSTM) model by leveraging the key properties of day-of-week to improve accuracy. This study collects a data set containing 235 hotels of the period from various online distribution channels and generates different time series data with the same day-of-week. This study verifies the proposed model through three baseline models, namely, autoregressive integrated moving average (ARIMA), support vector regression (SVR), and Naïve models. Findings shed light on how to lessen the impact of violent fluctuations by combining a rolling procedure with separate day-of-week time series for the hospitality industry. Hence, theoretical and managerial areas for hotel room demand forecasting are enriched on the basis of adjusting room pricing strategies for hoteliers in improving revenue management and making appropriate deals for customers in booking hotel rooms.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

scholarly journals Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4826
Author(s):  
Kai Zhou ◽  
Yixin Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Sensor Array ◽  
Time Series Data ◽  
Practical Significance ◽  
Series Data ◽  
Support Vector ◽  
Identification Performance ◽  
Short Term ◽  
Gas Identification

Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence- based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation.

scholarly journals Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

2020 ◽  
Vol 70 (6) ◽  
pp. 619-625
Author(s):  
Rizul Aggarwal ◽  
Anjali Goswami ◽  
Jitender Kumar ◽  
Gwyneth Abdiel Chullai
Keyword(s):  
Time Series ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Term Prediction

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.

scholarly journals Financial Time Series Forecasting Using Directed-Weighted Chunking SVMs

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yongming Cai ◽  
Lei Song ◽  
Tingwei Wang ◽  
Qing Chang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Data Set ◽  
Promising Alternative ◽  
Financial Time ◽  
Operation Speed ◽  
Support Vectors

Support vector machines (SVMs) are a promising alternative to traditional regression estimation approaches. But, when dealing with massive-scale data set, there exist many problems, such as the long training time and excessive demand of memory space. So, the SVMs algorithm is not suitable to deal with financial time series data. In order to solve these problems, directed-weighted chunking SVMs algorithm is proposed. In this algorithm, the whole training data set is split into several chunks, and then the support vectors are obtained on each subset. Furthermore, the weighted support vector regressions are calculated to obtain the forecast model on the new working data set. Our directed-weighted chunking algorithm provides a new method of support vectors decomposing and combining according to the importance of chunks, which can improve the operation speed without reducing prediction accuracy. Finally, IBM stock daily close prices data are used to verify the validity of the proposed algorithm.

scholarly journals Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

2020 ◽  
Vol 13 (1) ◽  
pp. 103
Author(s):  
Lena Chang ◽  
Yi-Ting Chen ◽  
Jung-Hua Wang ◽  
Yang-Lang Chang
Keyword(s):  
Time Series ◽  
Detection Efficiency ◽  
Experimental Results ◽  
The Other ◽  
Series Data ◽  
Support Vector ◽  
Time Interval ◽  
Detection Accuracy ◽  
Data Set ◽  
Decision Method

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.

scholarly journals LSTM-Based Forecasting for Urban Construction Waste Generation

2020 ◽  
Vol 12 (20) ◽  
pp. 8555
Author(s):  
Li Huang ◽  
Ting Cai ◽  
Ya Zhu ◽  
Yuliang Zhu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Structure Design ◽  
Time Series Forecasting ◽  
Series Data ◽  
Support Vector ◽  
Waste Generation ◽  
Short Term ◽  
Construction Waste

Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

RURAL INFRASTRUCTURAL DEVELOPMENT AND ECONOMIC GROWTH: A TIME-SERIES EVIDENCE FROM PAKISTAN (1981-2010)

2012 ◽  
Vol 02 (01) ◽  
pp. 51-57
Author(s):  
Rao Ishtiaq Ahmad ◽  
Shahnawaz Malik
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Rural Areas ◽  
Time Series Data ◽  
Series Data ◽  
Positive Role ◽  
Data Set ◽  
Infrastructural Development ◽  
The Impact ◽  
Determinants Of Economic Growth

Main purpose of this study is to find out the role of rural infrastructural development on economic growth of Pakistan. It has been hypothesized that rural infrastructural development has significant positive role for enhancement of economic growth. For the purpose of investigation we utilize such a model which may reflect the steady-state equilibrium differences in a Barro-type framework consisting of Solow type sets of variables and allow conditional convergence. On the basis of time series data set of Pakistan from 1981 to 2010, we employ OLS methodology so as to measure the impact of rural infrastructural development on economic growth of Pakistan. In view of limitations regarding categorization of data on regional basis, we use developmental public expenditures in rural areas as a proxy for rural infrastructural development. After analysis, we are of the view that rural infrastructural development has a positive role for economic growth of Pakistan, however, its role has found to be less significant in comparison to capital and labour as determinants of economic growth.

Oil Revenues and Export Earnings Instability: The Evidence from Iran

2015 ◽  
Vol 15 (3) ◽  
pp. 431-442
Author(s):  
Parviz Asheghian
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Time Series Data ◽  
Total Export ◽  
Series Data ◽  
Data Set ◽  
Price Variability ◽  
Oil Revenue ◽  
Earnings Instability ◽  
The Impact

As a member of OPEC, Iran is a nation that is dependent on petrodollars. More specifically, roughly 80 percent of total export earnings in Iran are generated from oil revenue. This in fact is one of the attributes of many developing countries in that their exports are concentrated in either one or a small number of primary products that contribute to the bulk of their foreign exchange revenues. Export instability occurs because export earnings tend to fluctuate annually to a greater extent for developing countries than for advanced countries. The factors that give rise to export instability can be classified as price variability and a high degree of commodity concentration. To date, no study has examined the impact of export instability in the highly oil-dependent Iran. This study develops a model and employs a forty-year annual time series data set to estimate the impact of commodity concentration and price variability in Iran. The estimation results obtained from the time-series model developed in this study does not support the conventional argument, regarding the positive correlation between commodity concentration and export instability. It also shows that fluctuations in petroleum export revenues have significant impact on total export earnings instability in Iran.

scholarly journals The Effect of Austerity Packages on Government Popularity During the Great Recession

2021 ◽  
pp. 1-19
Author(s):  
Abel Bojar ◽  
Björn Bremer ◽  
Hanspeter Kriesi ◽  
Chendi Wang
Keyword(s):  
Time Series ◽  
Great Recession ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Cumulative Impact ◽  
The Great Recession ◽  
Pooled Time Series ◽  
The Impact ◽  
Opinion Polls

Abstract During the Great Recession, governments across the continent implemented austerity policies. A large literature claims that such policies are surprisingly popular and have few electoral costs. This article revisits this question by studying the popularity of governments during the economic crisis. The authors assemble a pooled time-series data set for monthly support for ruling parties from fifteen European countries and treat austerity packages as intervention variables to the underlying popularity series. Using time-series analysis, this permits the careful tracking of the impact of austerity packages over time. The main empirical contributions are twofold. First, the study shows that, on average, austerity packages hurt incumbent parties in opinion polls. Secondly, it demonstrates that the magnitude of this electoral punishment is contingent on the economic and political context: in instances of rising unemployment, the involvement of external creditors and high protest intensity, the cumulative impact of austerity on government popularity becomes considerable.

Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data

2014 ◽  
Vol 18 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Hoang T. P. Thanh ◽  
◽  
Phayung Meesad ◽  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Text Mining ◽  
Stock Market ◽  
Stock Prices ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Support Vector ◽  
Data Set

Predicting the behaviors of the stock markets are always an interesting topic for not only financial investors but also scholars and professionals from different fields, because successful prediction can help investors to yield significant profits. Previous researchers have shown the strong correlation between financial news and their impacts to the movements of stock prices. This paper proposes an approach of using time series analysis and text mining techniques to predict daily stock market trends. The research is conducted with the utilization of a database containing stock index prices and news articles collected from Vietnam websites over 3 years from 2010 to 2012. A robust feature selection and a strong machine learning algorithm are able to lift the forecasting accuracy. By combining Linear Support Vector Machine Weight and Support Vector Machine algorithm, this proposed approach can enhance the prediction accuracy significantly above those of related research approaches. The results show that data set represented by 42 features achieves the highest accuracy by using one-against-one Support Vector Machines (up to 75%) and one-against-one method outperforms one-againstall method in almost all case studies.

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