A novel data-driven model for real-time influenza forecasting

Venna, Siva R.; Tavanaei, Amirhossein; Gottumukkala, Raju; Raghavan, Vijay V.; Maida, Anthony S.; Nichols, Stephen M.

doi:10.1101/185512

Cited by 15 publications

(15 citation statements)

References 51 publications

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“…For recent reviews of DNNs, see [3], [4]. DNNs have been remarkably successful in many applications including image recognition [1], [5], [6], object detection [7], [8], speech recognition [9], biomedicine and bioinformatics [10], [11], temporal data processing [12], and many other applications [4], [13], [14]. These recent advances in artificial intelligence (AI) have opened up new avenues for developing different engineering applications and understanding of how biological brains work [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Deep learning in spiking neural networks

et al. 2019

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1 In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation. Vast amounts of labeled training examples are required, but the resulting classification accuracy is truly impressive, sometimes outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and arguably the only viable option if one wants to understand how the brain computes. SNNs are also more hardware friendly and energy-efficient than ANNs, and are thus appealing for technology, especially for portable devices. However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents using backpropagation.Here we review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy, but also computational cost and hardware friendliness. The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNNs typically require many fewer operations.

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Section: Introductionmentioning

confidence: 99%

Deep learning in spiking neural networks

et al. 2019

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“…Recently, deep learning based LSTM has garnered significant attention for time series forecasting of various trends. LSTM has been previously employed to forecast: weather [9] , stock price movements [10] [11] , pandemics [12] , solar irradiance [13] [14] , atmospheric pollution levels [15] . They have also been employed to predict the answers to questions [16] , predicting the next word [17] etc .…”

Section: Introductionmentioning

confidence: 99%

Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach

Prasanth

Singh

Kumar

et al. 2021

Chaos, Solitons & Fractals

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“… Results indicated that the RF model outperformed the SVM and MARS and it can be utilized to diagnose the behavior of brucellosis over time. Venna et al [97 ] Influenza ARIMA, EAKF, LSTM Propose a novel data-driven machine learning method using long short-term memory-based multi-stage for influenza forecasting. Proposed method performs better than the existing well-known influenza forecasting methods (ARIMA and EAKF) and the results offer a promising direction to improve influenza forecasting.…”

Section: Resultsmentioning

confidence: 99%

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Ribeiro

Mariani

Coelho

2020

Journal of Biomedical Informatics

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Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making.

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A novel data-driven model for real-time influenza forecasting

Cited by 15 publications

References 51 publications

Deep learning in spiking neural networks

Deep learning in spiking neural networks

Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

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