Academic literature on the topic 'TBATS model'

The Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) model has been designed to estimate complex cyclical patterns (e.g., weeks within years) in time series data. This paper seeks to evaluate its applicability to educational data, daily school attendance in particular. Attendance rates in four high schools are analyzed over a ten year period using TBATS to illustrate the presence of both weekly and annual patterns in three of the schools and only weekly patterns in the fourth. The model features are explicated and it is shown how the estimation of weekly and annual cycles enhances the description of the data and improves our understanding of how the assessment of endogenous variability contributes to our understanding of daily high school attendance behavior. R script is provided in an appendix.

Abstract: One of the busiest airports in Indonesia managed by PT. Angkasa Pura I is Juanda International Airport. Besides, Juanda International Airport is also one of the gateways for the Indonesian state to other countries. The number of passengers at the departure terminal at Juanda International Airport from 2012 to 2013, both domestic and international routes, has increased by about 6.74%. Meanwhile, the arrival terminal experienced an increase of about 8.31%. From 2013 to 2014 the departure route decreased by 2.51%. Meanwhile, the arrival route decreased by 1.99%. In 2014 to 2015 the departure route decreased by 11.31%. Meanwhile, the arrival route decreased by 0.78%. There is an increase and decrease in the number of passengers at Juanda International Airport, it is necessary to research forecasting the number of passengers at Juanda International Airport, both from domestic routes and international routes. The purpose of this study is to balance number of passengers and number of flights in the future with the availability of the number of aircraft and airport capacity. The data used is data on the number of passengers each month at Juanda International Airport. Where the data was obtained from PT. Angkasa Pura I (Persero). The criteria for selecting the best model based on the RMSE value shows that the best model selected by the ARIMA model has 14 routes, while the selected Time Series Regression model has 9 routes and the selected TBATS model has 7 routes.Abstrak: Salah satu bandara tersibuk di Indonesia yang diolah PT. Angkasa Pura I adalah Bandara Internasional Juanda. Selain itu, Bandara Internasional Juanda juga merupakan salah satu pintu gerbang negara Indonesia menuju negara-negara lain. Jumlah penumpang di terminal keberangkatan Bandara Internasional Juanda dari tahun 2012 hingga tahun 2013 baik rute domestik maupun internasional mengalami peningkatan sekitar 6,74%. Sedangkan di terminal kedatangan mengalami kenaikan sekitar 8,31%. Pada tahun 2013 hingga tahun 2014 pada rute keberangkatan mengalami penurunan sebesar 2,51%. Sedangkan pada rute kedatangan mengalami penurunan sebesar 1,99%.Pada tahun 2014 hingga tahun 2015 pada rute keberangkatan mengalami penurunan sebesar 11,31%. Sedangkan pada rute kedatangan mengalami penurunan sebesar 0,78%. Adanya kenaikan dan penurunan jumlah penumpang di Bandara Internasional Juanda, maka perlu diadakan penelitian mengenai peramalan jumlah penumpang di Bandara Internasional Juanda, baik dari rute domestik maupun rute internasional. Tujuan dari penelitian ini adalah untuk menyeimbangkan jumlah penumpang dan jumlah penerbangan pada masa mendatang dengan ketersediaan jumlah pesawat dan kapasitas bandar udara.Data yang digunakan adalah data jumlah penumpang tiap bulan di Bandara Internasional Juanda. Dimana data tersebut diperoleh dari PT. Angkasa Pura I (Persero). Kriteria pemilihan model terbaik berdasarkan nilai RMSE menunjukkan bahwa Model terbaik yang terpilih model ARIMA terdapat 14 rute sedangkan yang terpilih model Regresi Time Series terdapat 9 rute dan yang terpilih model TBATS terdapat 7 rute.

Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

Abstract Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Muğla region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Muğla region, encouraging the time-varying coefficients extensions of the precipitation model.

The World Health Organization has urged countries to prioritize dementia in their public health policies. Dementia poses a tremendous socioeconomic burden, and the accurate prediction of the annual increase in prevalence is essential for establishing strategies to cope with its effects. The present study established a model based on the architecture of the long short-term memory (LSTM) neural network for predicting the number of dementia cases in Taiwan, which considers the effects of age and sex on the prevalence of dementia. The LSTM network is a variant of recurrent neural networks (RNNs), which possesses a special gate structure and avoids the problems in RNNs of gradient explosion, gradient vanishing, and long-term memory failure. A number of patients diagnosed as having dementia from 1997 to 2017 was collected in annual units from a data set extracted from the Health Insurance Database of the Ministry of Health and Welfare in Taiwan. To further verify the validity of the proposed model, the LSTM network was compared with three types of models: statistical models (exponential smoothing (ETS), autoregressive integrated moving average model (ARIMA), trigonometric seasonality, Box–Cox transformation, autoregressive moving average errors, and trend seasonal components model (TBATS)), hybrid models (support vector regression (SVR), particle swarm optimization–based support vector regression (PSOSVR)), and deep learning model (artificial neural networks (ANN)). The mean absolute percentage error (MAPE), root-mean-square error (RMSE), mean absolute error (MAE), and R-squared (R2) were used to evaluate the model performances. The results indicated that the LSTM network has higher prediction accuracy than the three types of models for forecasting the prevalence of dementia in Taiwan.

This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.