Evaluation of Bottom-Up and Top-Down Strategies for Aggregated Forecasts: State Space Models and ARIMA Applications

Soto-Ferrari, Milton; Chams-Anturi, Odette; Escorcia-Caballero, Juan P.; Hussain, Namra; Khan, Muhammad Khurram

doi:10.1007/978-3-030-31140-7_26

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…ETS models use a combination of exponential smoothing and state space modeling techniques to estimate the model's parameters and develop forecasts. ETS models are flexible and can handle various time series patterns, including trends, seasonality, and irregular fluctuations (28,29). A total of fifteen different extensions of ETS have been…”

Section: Etsmentioning

confidence: 99%

“…ARIMA encompassed a family class of models used to represent and predict autocorrelated and stochastic seasonal and non-seasonal time series composed of autoregressive (AR), moving average (MA), and differentiated integrated (I) baselines mixed with AR or MA processes (31). Even though ARIMA can be used for the modeling of a wide range of time series as well as for homogeneous non-stationary series (28,32), this technique has a significant limitation since it requires the assumption that the specified time series is linear or has a grade of smoothening as heterogeneous non-stationary time series are in most cases not likely to find reliable forecasts with this method (13,23,33).…”

Section: Arimamentioning

confidence: 99%

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Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Soto-Ferrari

Carrasco‐Pena

Prieto

2023

Preprint

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Background The Mpox virus is a disease of rare occurrence from the same family as the variola virus, which is hardly ever fatal, and its symptoms are like the ones of smallpox. As the outbreak was emerging in Brazil, the frail post-pandemic public health system and stigmatization yielded substandard data collection of confirmed cases. As of the beginning of 2023, the established case trend is short and has noisy patterns that challenge most existing forecasting methods.Methods To extend the modeling choices for emerging outbreaks with volatile and short-term confirmed case data, we evaluate the performance of multiple deep learning architectures, including Convolutional Neural Network (CNN), unidirectional Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). We further developed a bagged ensemble methodology (i.e., BaLSTM) with the best-identified method for predicting the Mpox emerging outbreak in Brazil. Our evaluation dataset consists of Brazil’s weekly Mpox cases from July 2022 to January 2023, contrasting the performance for the coming six weeks. Additionally, accepted machine-learning models were built to predict weekly confirmed cases to compare the relative performance of our implementation. We evaluated the performance of our deep learning architectures with exponential smoothing (ETS), ARIMA, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks Autoregression (NNAR).Results Based on the results, the BaLSTM approach achieves an accuracy of 80.83% when considering its associated prediction intervals, translating into a 45% improvement in measuring forecast errors for the Mpox Brazil cases when evaluated with the remaining assessed methods. The proposed model can capture trends and patterns in the time series while considering the intrinsic attributes of the sample.Conclusions Implementation such as those described in this research will become increasingly crucial in predicting emerging epidemics with a small sample size and an apparent uncertain behavior.

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Section: Etsmentioning

confidence: 99%

Section: Arimamentioning

confidence: 99%

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Soto-Ferrari

Carrasco‐Pena

Prieto

2023

Preprint

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“…Using an effective rate approach, the authors found out that actual VAT revenues constituted less than a half of potential VAT revenues. The econometric method establishes a strong empirical long-term relationship between VAT revenues and its base Soto-Ferrari et al (2019). examined the capabilities and efficiency of ARIMA models, forecasting capabilities, and developed procedures to improve such forecasting.…”

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confidence: 99%

Analysis and modelling of value added tax revenues on imports: Some issues of application in Ukraine

Luchko

Drozd

Plutytska

et al. 2021

Int. J. Prod. Manag. Eng.

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<p class="TtuloAbstract">The aim of the article is to study the issues of analysis, modeling with the purpose of forecasting the payment of value added tax (VAT) on goods, works and services imported as imports into the customs territory of Ukraine. The reliability and validity of the planned VAT rate depend on the assessment of the status, forecast, seasonality and trends of economic and social development. The purpose of the work is to analyze and systematize the methodology for modeling VAT revenues from imports, justify the use of the econometric method and develop an adequate ARIMA model. It application is possible in the long term as well as smaller periods of time, which is relevant for monitoring and control of tax revenues. The study revealed the main factors influencing the application of the ARIMA model when modeling VAT revenues from imports. The resulting regression model in STATISTICA linked the variables with an accurate approximation.</p>

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VAT Elasticities on Imports as a Lever to Forecast Collection: Mexico 2010–2021

Moreno Espinosa,

Mata Mata,

Beltrán-Godoy

2023

Creating Economic Stability Amid Global Uncertainty

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Evaluation of Bottom-Up and Top-Down Strategies for Aggregated Forecasts: State Space Models and ARIMA Applications

Cited by 7 publications

References 30 publications

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Analysis and modelling of value added tax revenues on imports: Some issues of application in Ukraine

VAT Elasticities on Imports as a Lever to Forecast Collection: Mexico 2010–2021

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