scholarly journals Short-Term Forecasts of COVID-19 Spread Across Indian States Until 1 May 2020

2020 ◽  
Author(s):  
Sarita Azad ◽  
Neeraj Poonia
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
The Other ◽  
Time Data ◽  
Short Term ◽  
Indian States ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Real Time Data ◽  
Exponential Smoothing Method

The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 21 April 2020. Applying Holt’s second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generate 10-day ahead forecasts of the likely number of infected cases and deaths in India for 22 April to 1 May 2020. Our results show that the number of cumulative cases in India will rise to 36335.63 [PI 95% (30884.56, 42918.87)], concurrently the number of deaths may increase to 1099.38 [PI 95% (959.77, 1553.76)] by 1 May 2020. Further, we have divided the country into severity zones based on the cumulative cases. According to this analysis, Maharashtra is likely to be the most affected states with around 9787.24 [PI 95% (6949.81, 13757.06)] cumulative cases by 1 May 2020. However, Kerala and Karnataka are likely to shift from the red zone (i.e. highly affected) to the lesser affected region. On the other hand, Gujarat and Madhya Pradesh will move to the red zone. These results mark the states where lockdown by 3 May 2020, can be loosened.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals SHORT-TERM TRAFFIC FLOW PREDICTION USING A METHODOLOGY BASED ON AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GENETIC PROGRAMMING

Transport ◽  
2016 ◽  
Vol 31 (3) ◽  
pp. 343-358 ◽  
Author(s):  
Chengcheng Xu ◽  
Zhibin Li ◽  
Wei Wang
Keyword(s):  
Genetic Programming ◽  
Traffic Flow ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Performance ◽  
Hybrid Models ◽  
Short Term ◽  
Time Intervals ◽  
Autoregressive Integrated Moving Average

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.

scholarly journals Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

2020 ◽  
Author(s):  
Messis Abdelaziz ◽  
Adjebli Ahmed ◽  
Ayeche Riad ◽  
Ghidouche Abderrezak ◽  
Ait-Ali Djida
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Arima Models ◽  
Forecast Period ◽  
Autoregressive Integrated Moving Average ◽  
Hospital Reform ◽  
Estimated Parameters ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

ABSTRACTCoronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

scholarly journals Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 260
Author(s):  
Ling Shen ◽  
Jian Lu ◽  
Dongdong Geng ◽  
Ling Deng
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Mode Decomposition ◽  
The Common ◽  
Model Training

Big data from toll stations provides reliable and accurate origin-destination (OD) pair information of expressway networks. However, although the short-term traffic prediction model based on big data is being constantly improved, the volatility and nonlinearity of peak traffic flow restricts the accuracy of the prediction results. Therefore, this research attempts to solve this problem through three contributions, firstly, proposing the use the Pauta criterion from statistics as the standard for defining the anomaly criteria of expressway traffic flows. Through comparison with the common local outlier factor (LOF) method, the rationality and advantages of the Pauta criterion were expounded. Secondly, adding week attributes to data, and splitting the data based on the similarity characteristics of traffic flow time series in order to improve the accuracy and efficiency of data input. Thirdly, by introducing empirical mode decomposition (EMD) to decompose the signal before autoregressive integrated moving average (ARIMA) model training is carried out. The first two contributions are for efficiency, the third is to deal with the volatility and nonlinearity of the abnormal peak training data. Finally, the model is analyzed, based on the expressway toll data of the Jiangsu Province. The results show that the EMD-ARIMA model has more advantages than the ARIMA model when dealing with fluctuating data.

scholarly journals ARIMA Model in Predicting Banking Stock Market Data

2018 ◽  
Vol 12 (11) ◽  
pp. 309 ◽  
Author(s):  
Mohammad Almasarweh ◽  
S. AL Wadi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Short Term Prediction

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

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