Forecasting hotel demand uncertainty using time series Bayesian VAR models

2018 ◽  
Vol 25 (5) ◽  
pp. 734-756 ◽  
Author(s):  
Apostolos Ampountolas
Keyword(s):  
Demand Uncertainty ◽  
Error Rates ◽  
Percentage Error ◽  
Revenue Maximization ◽  
Var Models ◽  
Forecasting Accuracy ◽  
Bayesian Var ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
The Mean

Demand uncertainty is a fundamental characteristic of the hospitality industry. Hotel room inventory is fixed, and devising an accurate daily demand measurement is a key operational challenge. In practice, it is difficult to predict the industry stability and capture demand uncertainty, so the industry relies on demand estimates. This process of estimation affects revenue maximization, as it is sensitive to incremental costs. In this article, we implemented vector autoregressive (VAR) models and compared them to the Bayesian VAR to examine the accuracy of predicting demand. We evaluated the results using a new measure of forecasting accuracy, the mean arctangent absolute percentage error (MAAPE). The results generated from the forecasts confirm the significant improvement in forecasting performance that can be obtained using the Bayesian model. It is noteworthy that the VAR performs the best for the lower horizons. The results also suggest that MAAPE outperforms other existing accuracy measures, in terms of error rates.

Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

2014 ◽  
Vol 529 ◽  
pp. 621-624
Author(s):  
Syang Ke Kung ◽  
Chi Hsiu Wang
Keyword(s):  
Time Series ◽  
Industrial Production ◽  
Multivariate Time Series ◽  
Performance Comparison ◽  
Power Transformation ◽  
Var Models ◽  
Bayesian Var ◽  
Forecasting Performance ◽  
The U.S ◽  
Forecasting Practice

This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive model and is recommendable to forecasting practice.

scholarly journals Vector Autoregressive Integrated (VARI) Method for Forecasting the Number of Internasional Visitor in Batam and Jakarta

2020 ◽  
Vol 17 (1) ◽  
pp. 94-108
Author(s):  
Septie Wulandary
Keyword(s):  
Univariate Analysis ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Separate Model ◽  
Forecasting Methods ◽  
Diagnostic Checking ◽  
Stationarity Test ◽  
The Mean ◽  
Model Formation ◽  
Order Determination

Forecasting methods that are often used are time series analysis, the Autoregressive (AR) method. The AR method only carries out univariate analysis, meaning that it carries out a separate model between the number of international visitor coming to Indonesia through Batam and Jakarta. Though there is a possibility, the number of international visitor arriving through Jakarta affects the number of international visitor arriving through Batam. Therefore, in this study the Vector Autoregressive Integrated (VARI) method is used. The VARI model is used on the number of international visitor arrivals per month at Batam and Jakarta for the period Januari 2014 – December 2019. VARI model formation through several stages, namely stationarity test, autoregressive order determination, VARI model formation, and diagnostic checking of the model. With the VARI model, VARI(5,1), the two significant simultaneously equation results are obtained. The Mean Absolute Percentage Error (MAPE) in this model are as follows 1,98% and 2,48% in predicting the number of international visitor arrivals in Batam and Jakarta. In this study also forecasting the number of international visitor arrivals in Batam and Jakarta in January – December 2020

scholarly journals An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 554 ◽  
Author(s):  
Tin-Chih Toly Chen ◽  
Yu-Cheng Wang ◽  
Chin-Hau Huang
Keyword(s):  
Random Access ◽  
Real Data ◽  
Product Yield ◽  
Percentage Error ◽  
Access Memory ◽  
Forecasting Accuracy ◽  
Meaningful Information ◽  
Forecasting Methods ◽  
Forecasting Performance ◽  
Partial Consensus

Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

PCA-BASED EX-ANTE FORECASTING OF SWAP TERM STRUCTURES

2009 ◽  
Vol 12 (04) ◽  
pp. 465-489 ◽  
Author(s):  
OLIVER BLASKOWITZ ◽  
HELMUT HERWARTZ
Keyword(s):  
Term Structure ◽  
Principal Component ◽  
Cash Flows ◽  
Data Driven ◽  
Selection Strategy ◽  
Adaptive Model ◽  
Var Models ◽  
Vector Autoregressive ◽  
Term Structures ◽  
Forecasting Performance

In this study, we forecast the term structure of EURIBOR swap rates by means of rolling vector autoregressive (VAR) models. In advance, a principal component analysis (PCA) is adopted to reduce the dimensionality of the term structure. To statistically assess the forecasting performance for particular rates and the level, slope and curvature of the swap term structure, we rely on the Henrikkson–Merton statistic. Economic performance is investigated by means of cash flows implied by alternative trading strategies. Finally, a data-driven, adaptive model selection strategy to "predict the best forecasting model" out of a set of 100 alternative PCA/VAR implementations is shown to outperform forecasting schemes that rely on global homogeneity of the term structure.

scholarly journals The date predicted 200.000 cases of Covid-19 in Spain

2020 ◽  
Vol 2 (2) ◽  
pp. 188-193 ◽  
Author(s):  
Ansari Saleh Ahmar ◽  
Eva Boj
Keyword(s):  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Forecasting Accuracy ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Accuracy Measures

The aim of this study is to predict 200.000 cases of Covid-19 in Spain. Covid-19 Spanish confirmed data obtained from Worldometer from 01 March 2020 – 17 April 2020. The data from 01 March 2020 – 10 April 2020 using to fitting with data from 11 April – 17 April 2020. For the evaluation of the forecasting accuracy measures, we use the mean absolute percentage error (MAPE). Based on the results of SutteARIMA fitting data, the accuracy of SutteARIMA for the period 11 April 2020 - 17 April 2020 is 0.61% and we forecast 20.000 confirmed cases of Spain by the WHO situation report day 90/91 which is 19 April 2020 / 20 April 2020.

scholarly journals Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4839
Author(s):  
Aritz Bilbao-Jayo ◽  
Aitor Almeida ◽  
Ilaria Sergi ◽  
Teodoro Montanaro ◽  
Luca Fasano ◽  
...  
Keyword(s):  
Behavior Modeling ◽  
Percentage Error ◽  
Location Prediction ◽  
Indoor Environments ◽  
Prediction System ◽  
Indoor Location ◽  
The Mean ◽  
Locating System ◽  
Monitoring Devices ◽  
Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals Agrometeorological models for estimating sweet cassava yield

2018 ◽  
Vol 48 (1) ◽  
pp. 43-51
Author(s):  
Victor Brunini Moreto ◽  
Lucas Eduardo de Oliveira Aparecido ◽  
Glauco de Souza Rolim ◽  
José Reinaldo da Silva Cabral de Moraes
Keyword(s):  
Great Influence ◽  
Soil Water Storage ◽  
Percentage Error ◽  
P Value ◽  
Climate Conditions ◽  
Paulo State ◽  
State Water ◽  
The Mean ◽  
Sweet Cassava ◽  
Main Factor

ABSTRACT Brazil is the fourth largest producer of cassava in the world, with climate conditions being the main factor regulating its production. This study aimed to develop agrometeorological models to estimate the sweet cassava yield for the São Paulo state, as well as to identify which climatic variables have more influence on yield. The models were built with multiple linear regression and classified by the following statistical indexes: lower mean absolute percentage error, higher adjusted determination coefficient and significance (p-value < 0.05). It was observed that the mean air temperature has a great influence on the sweet cassava yield during the whole cycle for all regions in the state. Water deficit and soil water storage were the most influential variables at the beginning and final stages. The models accuracy ranged in 3.11 %, 6.40 %, 6.77 % and 7.15 %, respectively for Registro, Mogi Mirim, Assis and Jaboticabal.

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