Multi-step CNN forecasting for COVID-19 multivariate time-series

Abstract

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.

Optimal Routing in Supply Chain Network Design

“…The utilization of this forecasting technique is advantageous for perishable commodities such as fresh bananas because it places greater emphasis on the most recent demand data. This enables prompt adaptation to short-term fluctuations in demand [17], as presented in Table 1.…”

“…The mean absolute percentage error (MAPE) method was used to define the deviation of the error values from five values [17,19]. The formulation used is as follows:…”

Using Traffic Sensors in Smart Cities to Enhance a Spatio-Temporal Deep Learning Model for COVID-19 Forecasting

“…Shahid et al [22] used similar models to estimate deaths and recoveries in 10 major countries showing that the bidirectional LSTM (Bi-LSTM) model was able to generate more accurate predictions than other non-time series-oriented machine learning models such as Support Vector Regression (SVR). Haviluddin et al [23] analyzed different variants of Convolutional Neural Networks (CNN) used in order to extract patterns in temporal COVID-19 data showing their performance when using several loss functions. The authors in [24] proposed to combine LSTM and CNN models over time series data for COVID-19 forecasting, showing that such combined models were able to outperform either LSTM-based or CNN-based models.…”

“…Predicted Variable [6,12,13] Regression Trees, Gaussian processes Traffic volumes [15][16][17][18] Shallow ML models COVID-19 diagnoses [19,20] Shallow ML models COVID-19 incidence [21][22][23] Deep ML models COVID-19 incidence [11,[25][26][27] Space-time models Mobility-enhanced COVID-19 estimations [30][31][32][33][34] Shallow ML models Mobility estimations caused by COVID-19…”