Research and Prediction on China's Novel Coronavirus (2019-nCoV/ COVID-19) Epidemic——Based on Time Series ARIMA Model

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

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Forecasting Analysis of Covid-19 Cases with Wavelet Neural Network and Time Series Approach

International Journal of Neural Networks and Advanced Applications ◽

10.46300/91016.2021.8.2 ◽

2021 ◽

Vol 8 ◽

pp. 6-11

Author(s):

Asli Kaya ◽

Fatih Cemrek ◽

Ozer Ozdemir

Keyword(s):

Neural Networks ◽

Time Series ◽

Moving Average ◽

Arima Model ◽

Wavelet Neural Networks ◽

Government Officials ◽

Response Planning ◽

Time Series Approach ◽

Novel Coronavirus ◽

Different Response

COVID-19 is a respiratory disease caused by a novel coronavirus first detected in December 2019. As the number of new cases increases rapidly, pandemic fatigue and public disinterest in different response strategies are creating new challenges for government officials in tackling the pandemic. Therefore, government officials need to fully understand the future dynamics of COVID-19 to develop strategic preparedness and flexible response planning. In the light of the above-mentioned conditions, in this study, autoregressive integrated moving average (ARIMA) time series model and Wavelet Neural Networks (WNN) methods are used to predict the number of new cases and new deaths to draw possible future epidemic scenarios. These two methods were applied to publicly available data of the COVID-19 pandemic for Turkey, Italy, and the United Kingdom. In our analysis, excluding Turkey data, the WNN algorithm outperformed the ARIMA model in terms of forecasting consistency. Our work highlighted the promising validation of using wavelet neural networks when making predictions with very few features and a smaller amount of historical data.

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Time Series Analysis of The Novel Coronavirus (COVID-19)

Journal of Immunology and Allergy ◽

10.37191/mapsci-2582-6549-1(3)-015 ◽

2020 ◽

Cited By ~ 1

Author(s):

Bin Zhao

Keyword(s):

United States ◽

Time Series ◽

Arima Model ◽

The United States ◽

Hubei Province ◽

Cumulative Number ◽

The Novel ◽

Short Term ◽

The Real ◽

Novel Coronavirus

Background: Since the first appearance of the novel coronavirus in Wuhan in December 2019, it has quickly swept the world and become a major security incident facing humanity today. While the novel coronavirus threatens people's lives and safety, the economies of various countries have also been severely damaged. Due to the epidemic, a large number of enterprises have faced closures, employment has become more difficult, and people's lives have been greatly affected. Therefore, to establish a time series model for Hubei Province, where the novel coronavirus first broke out, and the United States, where the epidemic is most severe, to analyze the spreading trend and short-term forecast of the new coronavirus, which will help countries better understand the development trend of the epidemic and make more adequate preparation and timely intervention and treatment to prevent the further spread of the virus. Methods: For the data collected from Hubei Province, including cumulative diagnoses, cumulative deaths, and cumulative cures, we use SPSS to establish the time series model. Since there is no problem of missing data values, we define days as the time variable, remove outliers, and set the width of the confidence interval to 95% for prediction, then use SPSS's expert modeler to find the best-fit model for each sequence. ACF, PACF graphs of the residuals, and Q-tests are used to determine whether the residuals are white noise sequences and to check whether the model is a suitable model. Holt model is used for the cumulative number of diagnoses, and ARIMA (1,2,0) model is used for cumulative cures and deaths. Similarly, we also collect data for the US, including the cumulative number of diagnoses, cumulative deaths, and cumulative cures. For the three groups mentioned above, ARIMA (2,2,6) model, ARIMA (0,2,0) model, and ARIMA (0,2,1) model is used respectively. Findings: From our modeling of the data, the time series diagrams of the real the fitted data almost overlap, so the fitting effect of the Holt model and the ARIMA model we use is very suitable. We compare the predicted values with the real values of the same period and find that the epidemic situation in Hubei Province has basically ended after May, but the epidemic situation in the United States has become more severe after May, so the Holt model and the ARIMA model are also very appropriate in predicting the epidemic situation in short-term. Interpretation: Because the Chinese government has always put the safety of people’s lives in the first place, when the epidemic broke out, it decisively closed the city of Hubei Province. One side is in trouble, all sides support, they concentrate all resources of whole country to save Hubei Province at the expense of the economy only in order to save more people. Now we can clearly see that the epidemic has been controlled in China and the whole country is developing in a good direction. The situation in the United States, on the other hand, is also influenced by the social environment.

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ARIMA modelling of predicting COVID-19 infections

10.1101/2020.04.18.20070631 ◽

2020 ◽

Cited By ~ 4

Author(s):

W.Regis Anne ◽

S.Carolin Jeeva

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Information Criterion ◽

World Health ◽

Director General ◽

Global Pandemic ◽

Novel Coronavirus ◽

Health Organization ◽

Short Term Prediction

AbstractThe World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.

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Comparative Analysis of Deep Learning and Statistical methods for Covid-19 Cases Prediction

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37716 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2697-2701

Author(s):

Shubhnesh Kumar Goyal

Keyword(s):

Time Series ◽

Deep Learning ◽

Arima Model ◽

Comparative Approach ◽

Learning Approaches ◽

Short Term ◽

Time Series Modelling ◽

To Come ◽

Novel Coronavirus ◽

Good Agreement

Abstract: COVID-19 or novel coronavirus has affected the entire world causing widespread damages to human lives. Mathematicians and Statisticians have been trying to model the epidemiology of this disease to come with models that can predict its spread and outbreaks. Due to availability of time series like data in the case of this disease’s spread, Deep Learning and Statistical models like ARIMA have been strong contenders for the prediction modelling. This paper presents an explorative and comparative approach to these methods comparing results of LSTM and CNN models for short term predictions in India during April 2020, and exploring the use of ARIMA models in the context. This study suggests that while LSTM outperforms CNN in simple time series modelling of cases registered, with inclusion of more volume of data features, CNN tend to outperform LSTM. Also, ARIMA model predictions done over Spain and Italy are found to be in very good agreement with the reported cases, suggesting an alternative method to Deep Learning approaches for short term predictions. Keywords: Deep Learning, CNN, ARIMA, COVID-19 prediction, Epidemiology, Optimisation

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Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽

10.3390/math9101122 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1122

Author(s):

Oksana Mandrikova ◽

Nadezhda Fetisova ◽

Yuriy Polozov

Keyword(s):

Neural Network ◽

Time Series ◽

Hybrid Model ◽

Complex Structure ◽

Arima Model ◽

Ionospheric Parameter ◽

Arima Models ◽

Complicated Structure ◽

Threshold Functions ◽

Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽

10.3390/en14113299 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3299

Author(s):

Ashish Shrestha ◽

Bishal Ghimire ◽

Francisco Gonzalez-Longatt

Keyword(s):

Time Series ◽

Kinetic Energy ◽

Power System ◽

Bayesian Model ◽

Arima Model ◽

Power Converter ◽

Series Data ◽

Percentage Error ◽

Optimal Size ◽

Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

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Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

Journal of Hydroinformatics ◽

10.2166/hydro.2017.035 ◽

2017 ◽

Vol 19 (2) ◽

pp. 261-281 ◽

Cited By ~ 7

Author(s):

Sahbi Boubaker

Keyword(s):

Time Series ◽

Particle Swarm Optimization ◽

Water Demand ◽

Moving Average ◽

Arima Model ◽

Particle Swarm ◽

Demand System ◽

Swarm Optimization ◽

Municipal Water ◽

Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

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Towards optimal ATM cash replenishment using time series analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201953 ◽

2021 ◽

pp. 1-13

Author(s):

Muhammad Rafi ◽

Mohammad Taha Wahab ◽

Muhammad Bilal Khan ◽

Hani Raza

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Automatic Teller Machine ◽

Proposed Model ◽

Auto Regressive ◽

Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

Computational Intelligence and Neuroscience ◽

10.1155/2021/1026978 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shaobo Lu

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Error Correction ◽

Relative Error ◽

Bp Neural Network ◽

Arima Model ◽

Forecast Model ◽

Price Prediction ◽

The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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