Forecasting disaggregated tourist arrivals in Croatia

2016 ◽  
Vol 23 (1) ◽  
pp. 78-98 ◽  
Author(s):  
Nicholas Apergis ◽  
Andrea Mervar ◽  
James E. Payne
Keyword(s):  
Fourier Transformation ◽  
Moving Average ◽  
The Other ◽  
Autoregressive Moving Average ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Models ◽  
Seasonal Time Series ◽  
Out Of Sample ◽  
The City

This study examines the performance of four alternative univariate seasonal time series forecasting models (seasonal autoregressive integrated moving average [SARIMA], SARIMA with Fourier transformation, ARAR, and fractionally integrated autoregressive-moving average) of tourist arrivals to 20 Croatian counties and the City of Zagreb. Both in-sample and out-of-sample forecasts reveal that the SARIMA model with Fourier transformation consistently outperforms the other models across the respective regions investigated.

scholarly journals Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Didin Muhjidin ◽  
Tedjo Sukmono
Keyword(s):  
Moving Average ◽  
Market Demand ◽  
Average Model ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Time Series ◽  
Moving Average Model ◽  
Make To Stock ◽  
Market Needs ◽  
Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

scholarly journals Hybrid SARIMA-FFNN model in forecasting cash outflow and inflow

2021 ◽  
Vol 2106 (1) ◽  
pp. 012002
Author(s):  
M Monica ◽  
A Suharsono ◽  
B W Otok ◽  
A Wibisono
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Feedforward Neural Network ◽  
Economic Life ◽  
Series Data ◽  
Sarima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Time Series ◽  
Cash Inflow

Abstract The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.

scholarly journals Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

scholarly journals PENGGUNAAN GENERALIZED LINEAR AUTOREGRESSIVE MOVING AVERAGE (GLARMA) DALAM ANALISIS FENOMENA RESESI DI INDONESIA TAHUN 2008Q1 – 2020Q1

2021 ◽  
Vol 2020 (1) ◽  
pp. 529-538
Author(s):  
Dimas Maladzi Wibawa ◽  
Nucke Widowati Kusumo Projo
Keyword(s):  
Moving Average ◽  
Autoregressive Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Fund Rate

Resesi merupakan penurunan secara signifikan dalam kegiatan ekonomi yang tersebar di seluruh aspek ekonomi. Resesi yang berkepanjangan dapat membawa perekonomian ke arah depresi. Indonesia termasuk ke dalam kategori fragile country yang menyebabkan kerentanan untuk masuk ke masa resesi semakin besar. Resesi merupakan bagian dari siklus bisnis yang mungkin akan dialami pada suatu waktu. Penelitian ini menggunakan model ­Generalized Linear Autoregressive Moving Average (GLARMA) untuk mengakomodir prediksi peluang dari fase resesi yang di definisikan dengan metode Bry Boschan dan meramal variabel independen dengan Autoregressive Integrated Moving Average (ARIMA). Variabel yang digunakan yaitu laju inflasi, fed fund rate, transaksi berjalan, harga minyak dunia, dan selisih U.S. 10Year-Bond dengan 3-Month LIBOR. Dari hasil penandaan siklus bisnis pada Produk Domestik Bruto riil, Indonesia mengalami delapan kali resesi sejak tahun 1993Q1-2020Q1 dengan durasi terpendek selama dua triwulan dan terpanjang selama delapan triwulan. Hasil dari model GLARMA(1,0) menunjukkan bahwa resesi di Indonesia didominasi oleh faktor eksternal yang dalam penelitian ini adalah selisih U.S. 10Year-Bond dengan 3-Month LIBOR dan fed fund rate memiliki pengaruh negatif secara signifikan terhadap resesi. Autoregressive lag-1 memiliki pengaruh positif terhadap resesi atau dengan kata lain kondisi yang terjadi pada triwulan sebelumnya berpengaruh terhadap terjadinya resesi di triwulan selanjutnya. Resesi di Indonesia diprediksi terjadi pada 2020Q3.

scholarly journals Time-Series Statistical Model for Forecasting Revenue and Risk Management

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mihir Kelkar ◽  
Cosmin Borsa ◽  
Lina Kim
Keyword(s):  
Time Series ◽  
Low Cost ◽  
Moving Average ◽  
Business Strategies ◽  
Annual Reports ◽  
Fiscal Year ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Low Cost Carrier ◽  
Southwest Airlines

Following a Low-Cost Carrier (LCC) model, Southwest Airlines has consistently demonstrated growing annual revenues up until the start of the COVID-19 pandemic. Southwest’s quarterly revenue shows that there exists a strong seasonal component with the revenue in the first quarter of the fiscal year (September) significantly higher than other quarters. Using the quarterly revenue data we constructed a time-series model: a seasonal autoregressive integrated moving average (SARIMA) model to forecast Southwest’s revenue over 2020. We then performed a cost and solvency risk analysis using the company’s financial results from its annual reports to analyze Southwest’s financial performance due to COVID-19, and proposed business strategies to keep Southwest financially stable.

scholarly journals Peramalan Tingkat Penghunian Tempat Tidur Hotel Bintang Tiga Kota Surakarta Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)

2019 ◽  
Vol 2 (1) ◽  
pp. 53
Author(s):  
Shindy Dwi Pratiwi
Keyword(s):  
Moving Average ◽  
Hotel Industry ◽  
Occupancy Rate ◽  
Autoregressive Integrated Moving Average ◽  
Occupancy Rates ◽  
The City

<p>Surakarta is a cultural city that is now starting to attract domestic and foreign tourists. This makes many tourists visit the city of Surakarta so that it affects the occupancy rate of hotels in Surakarta. The occupancy rate of hotels in Surakarta has fluctuations from each year. The uncertainty of hotel occupancy rates in Surakarta will certainly affect investors to choose policies in the hotel industry so that hotel occupancy rates in Surakarta City need to be estimated for the next year. In this study, the Autoregressive Integrated Moving Average (ARIMA) method was used to forecast hotel occupancy rates in Surakarta from January to May 2018. By using the best model IMA (1.1), it was concluded that the occupancy rate of three-star Surakarta hotels increased every the month.</p><p><strong>Keywords</strong><strong> : </strong>occupancy rate of hotel, forecasting, ARIMA.</p>

Estimating the Impact of Non-pharmaceutical Interventions Against COVID-19 on Mumps Incidence in Sichuan, China

2021 ◽  
Author(s):  
Wenqiang Zhang ◽  
Rongsheng Luan
Keyword(s):  
Public Health ◽  
Respiratory Virus ◽  
Moving Average ◽  
Sarima Model ◽  
Potential Reduction ◽  
Autoregressive Integrated Moving Average ◽  
Health Measures ◽  
Virus Incidence ◽  
The Impact ◽  
Pharmaceutical Interventions

Abstract Background: A series of social and public health measures have been implemented to contain coronavirus disease 2019 (COVID-19) in China. We examined the impact of non-pharmaceutical interventions against COVID-19 on mumps incidence as an agent to determine the potential reduction in other respiratory virus incidence.Methods: We modelled mumps incidence per month in Sichuan using a seasonal autoregressive integrated moving average (SARIMA) model, based on the reported number of mumps cases per month from 2017-2020. Results: The epidemic peak of mumps in 2020 is lower than in the preceding years. Whenever compared with the projected cases or the average from corresponding periods in the preceding years (2017-2019), the reported cases in 2020 markedly declined (P<0.001). From January to December, the number of mumps cases was estimated to decrease by 36.3% (33.9% - 38.8%), 34.3% (31.1% - 37.8%), 68.9% (66.1% - 71.6%), 76.0% (73.9% - 77.9%), 67.0% (65.0% - 69.0%), 59.6% (57.6% - 61.6%), 61.1% (58.8% - 63.3%), 49.2% (46.4% - 52.1%), 24.4% (22.1% - 26.8%), 30.0% (27.5% - 32.6%), 42.1% (39.6% - 44.7%), 63.5% (61.2% - 65.8%), respectively. The total number of mumps cases in 2020 was estimated to decrease by 53.6% (52.9% - 54.3%).Conclusion: Our study shows that non-pharmaceutical interventions against COVID-19 have had an effective impact on mumps incidence in Sichuan, China.

scholarly journals Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1848 ◽  
Author(s):  
Yuehjen Shao ◽  
Yi-Shan Tsai
Keyword(s):  
Moving Average ◽  
Multivariate Adaptive Regression Splines ◽  
Predictor Variables ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Explanatory Variables ◽  
Forecasting Models ◽  
Hybrid Approaches ◽  
The Future ◽  
Hybrid Forecasting

Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models.

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