International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure

An airport’s international passenger arrivals are susceptible to exogenous and endogenous factors (such as economic conditions, flight services, fluctuations and shocks). Accurate and reliable airport passenger demand forecasts are imperative for policymaking and planning by airport and airline management as well as by tourism authorities and operators. This article employs the Box–Jenkins SARIMA, SARIMAX and SARIMAX/EGARCH volatility models to forecast international passenger arrivals for the eight key Australian airports (Adelaide, Brisbane, Cairns, Darwin, Gold Coast, Melbourne, Perth and Sydney). Monthly international tourist arrivals between January 2006 and September 2012 are used for the empirical analysis. All the forecasting models are highly accurate with the lower values of mean absolute percentage error, mean absolute error and root mean squared error. The findings suggest that the international passenger arrivals of Australian airports are affected by positive and negative shocks and tourism marketing expenditure is also a significant factor influencing the majority of Australian airports’ international passenger arrivals.

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Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Energies ◽

10.3390/en14134046 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4046

Author(s):

Andrei M. Tudose ◽

Irina I. Picioroaga ◽

Dorian O. Sidea ◽

Constantin Bulac ◽

Valentin A. Boicea

Keyword(s):

Power Systems ◽

Mean Squared Error ◽

Load Forecasting ◽

Absolute Error ◽

Percentage Error ◽

Linear Regression Method ◽

Short Term ◽

Short Term Load Forecasting ◽

The Mean ◽

The Impact

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

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ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

GeoJournal of Tourism and Geosites ◽

10.30892/gtg.362spl19-701 ◽

2021 ◽

Vol 36 (2spl) ◽

pp. 708-714

Author(s):

Sayed Mohibul HOSSEN ◽

◽

Mohd Tahir ISMAIL ◽

Mosab I. TABASH ◽

Ahmed ABOUSAMAK ◽

...

Keyword(s):

Sustainable Development ◽

Mean Squared Error ◽

Moving Average ◽

Critical Role ◽

Absolute Error ◽

Tourism Industry ◽

Percentage Error ◽

Absolute Deviation ◽

Autoregressive Integrated Moving Average ◽

Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

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Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽

10.11113/matematika.v35.n4.1263 ◽

2019 ◽

Vol 35 (4) ◽

pp. 53-64

Author(s):

Siti Nabilah Syuhada Abdullah ◽

Ani Shabri ◽

Ruhaidah Samsudin

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Forecast Errors ◽

Ann Model ◽

Mode Decomposition ◽

The Neural Network ◽

Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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RNN-based Dimensional Speech Emotion Recognition

10.31227/osf.io/wa3vp ◽

2020 ◽

Author(s):

Bagus Tris Atmaja

Keyword(s):

Emotion Recognition ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Recognition System ◽

Speech Emotion Recognition ◽

Percentage Error ◽

Concordance Correlation ◽

Acoustic Feature ◽

Dense System

◆ A speech emotion recognition system based on recurrent neural networks is developed using long short-term memory networks.◆ Two of acoustic feature sets are evaluated: 31 Features (3 time-domain features, 5 frequency-domain features, 13 MFCCs, 5 F0s, and 5 Harmonics) and eGeMaps feature set (23 features).◆ To evaluate the performance, some metrics are used i.e. mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and concordance correlation coefficient (CCC). Among those metrics, CCC is main focus as it is used by other researchers.◆ The developed system used multi-task learning to maximize arousal, valence, and dominance at the same time using CCC loss (1 - CCC). The result shows using LSTM networks improve the CCC score compared to baseline dense system. The best CCC score isobtained on arousal followed by dominance and valence.

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Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil

International Forestry Review ◽

10.1505/146554819825863735 ◽

2019 ◽

Vol 21 (1) ◽

pp. 51-61 ◽

Cited By ~ 1

Author(s):

D.A. Buratto ◽

R. Timofeiczyk Junior ◽

J.C.G.L. Silva ◽

J.R. Frega ◽

M.S.S.A. Wiecheteck ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Mean Squared Error ◽

Arima Model ◽

Secondary Data ◽

Information Criterion ◽

Absolute Error ◽

Percentage Error ◽

Explanatory Variables ◽

Artificial Neural

The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine in Brazil for the period between 2017 and 2020.

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Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103520 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3520 ◽

Cited By ~ 11

Author(s):

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Hong Fan ◽

Laith Abualigah ◽

Mohamed Abd Elaziz

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Coefficient Of Determination ◽

Percentage Error ◽

Inference System ◽

Infected People ◽

Marine Predators ◽

Forecasting Performance ◽

The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

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COVID-19 Infection Forecasting based on Deep Learning in Iran

10.1101/2020.05.16.20104182 ◽

2020 ◽

Cited By ~ 3

Author(s):

Mehdi Azarafza ◽

Mohammad Azarafza ◽

Jafar Tanha

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Moving Average ◽

National Level ◽

Absolute Error ◽

Percentage Error ◽

Autoregressive Integrated Moving Average ◽

Error Metrics ◽

Squared Error ◽

Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

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Reconstructing Spectra from RGB Images by Relative Error Least-Squares Regression

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2020.28.42 ◽

2020 ◽

Vol 2020 (28) ◽

pp. 264-269

Author(s):

Yi-Tun Lin ◽

Graham D. Finlayson

Keyword(s):

Least Squares ◽

Relative Error ◽

Mean Squared Error ◽

Evaluation Criteria ◽

Absolute Error ◽

Percentage Error ◽

Least Squares Regression ◽

Worst Case ◽

Regression Methods ◽

Spectral Reconstruction

Spectral reconstruction (SR) algorithms attempt to map RGB- to hyperspectral-images. Classically, simple pixel-based regression is used to solve for this SR mapping and more recently patch-based Deep Neural Networks (DNN) are considered (with a modest performance increment). For either method, the 'training' process typically minimizes a Mean-Squared-Error (MSE) loss. Curiously, in recent research, SR algorithms are evaluated and ranked based on a relative percentage error, so-called MeanRelative-Absolute Error (MRAE), which behaves very differently from the MSE loss function. The most recent DNN approaches - perhaps unsurprisingly - directly optimize for this new MRAE error in training so as to match this new evaluation criteria.<br/> In this paper, we show how we can also reformulate pixelbased regression methods so that they too optimize a relative spectral error. Our Relative Error Least-Squares (RELS) approach minimizes an error that is similar to MRAE. Experiments demonstrate that regression models based on RELS deliver better spectral recovery, with up to a 10% increment in mean performance and a 20% improvement in worst-case performance depending on the method.

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ARIMA Model Selection for Composite Stock Price Index in Indonesia Stock Exchange

International Journal of Accounting and Finance Studies ◽

10.22158/ijafs.v2n1p31 ◽

2019 ◽

Vol 2 (1) ◽

pp. p31

Author(s):

Zul Amry ◽

Budi Halomoan Siregar

Keyword(s):

Stock Price ◽

Price Index ◽

Mean Squared Error ◽

Stock Exchange ◽

Arima Model ◽

Composite Index ◽

Information Criterion ◽

Absolute Error ◽

Percentage Error ◽

Stock Price Index

Composite Stock Price Index (CSPI) can be used as a reflection of the national economic condition of a country because it is an indicator to know the development the capital market in a country. Therefore, the movement in the future needs to be forecast. This study aims to build a model for the time series forecasting of Indonesia Composite Index (ICI) using the ARIMA model. The data used is the monthly data of ICI in Indonesia Stock Exchange (IDX) from January 2000 until December 2017 as many as 216 data. The method used in this research is the Box-Jenkins method. The autocorrelation (ACF) and partial autocorrelation function (PACF) are used for stationary test and model identification. The maximum estimated likelihood is used to estimate the parameter model. In addition, to select a model then used Akaike’s Information Criterion (AIC). Ljung-Box Q statistics are used for diagnostic tests. In addition, to show the accuracy of the model, we use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and the most appropriate model is ARIMA (0, 1, 1).

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A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

10.21203/rs.3.rs-83375/v1 ◽

2020 ◽

Author(s):

Sohail Saif ◽

Priya Das ◽

Suparna Biswas

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Bees Algorithm ◽

Percentage Error ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy ◽

Proposed Model ◽

Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

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