scholarly journals A Time Series Model on the Occurrence of COVID-19 Pandemic in Nigeria

2021 ◽  
pp. 66-80
Author(s):  
David Adugh Kuhe ◽  
Jonathan Atsua Ikughur
Keyword(s):  
Time Series ◽  
Model Building ◽  
Unit Root Test ◽  
Large Family ◽  
Information Criterion ◽  
Generalized Least Squares ◽  
Time Series Model ◽  
Percentage Error ◽  
Daily Number ◽  
Modeling And Forecasting

Coronaviruses belong to a large family of viruses which affect the hepatic, gastrointestinal, neurological and respiratory systems. The increase in the daily number of COVID-19 confirmed and deaths cases from different countries of the world has brought social, economic and political activities to a standstill, affecting individuals, government, public and private sectors. In this study, autoregressive integrated moving average (ARIMA) time series model for modeling and forecasting daily confirmed, recovered, and deaths cases of COVID-19 in Nigeria was used with data on daily cases of confirmed, recovered and deaths due to COVID-19 in Nigeria from 27/02/2020-31/07/2020 obtained from Nigeria Centre for Disease Control (NCDC) website. The data from 27/02/2020-16/07/2020 were used for model building while 15 observations from 17/07/2020-31/07/2020 were used for training and forecast evaluations. Time plots and Dickey-Fuller Generalized Least Squares unit root test were used to investigate the stationarity properties of the data. Schwarz Information Criterion (SIC) in conjunction with log likelihood were used to search for optimal ARIMA models while Mean Absolute Percentage Error (MAPE) was used for forecast evaluation.  Results showed that all the study variables were differenced stationary and hence integrated of order one, I (1). ARIMA (2,1,4), ARIMA (2,1,2) and ARIMA (2,1,3) models were selected as the best candidates for modeling and forecasting the confirmed, recovered and deaths cases of COVID-19 in Nigeria respectively. The study found an approximate COVID-19 life cycle of 12 days among the infected population. The 15 days’ forecasts from ARIMA (2,1,4) and ARIMA (2,1,2) models showed increases in the daily number of confirmed and recovered cases of COVID-19 in Nigeria. The forecasts from ARIMA (2,1,3) model however showed fluctuating trend with decline in the number of deaths cases due to the disease. The result of the study further showed that improving on the present approach to treatment will further decrease the number of casualties due to COVID-19 in Nigeria.

Stationarity tests in time series model building

1983 ◽  
Vol 2 (3) ◽  
pp. 249-257 ◽  
Author(s):  
Mukhtar M. Ali ◽  
Richard Thalheimer
Keyword(s):  
Time Series ◽  
Model Building ◽  
Time Series Model ◽  
Stationarity Tests

scholarly journals Modeling and Forecasting Periodic Time Series data with Fourier Autoregressive Model

2019 ◽  
pp. 1367-1373
Author(s):  
Abass I. Taiwo ◽  
Timothy Olabisi Olatayo ◽  
Adedayo Funmi Adedotun ◽  
Kazeem kehinde Adesanya
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Autoregressive Model ◽  
Estimation Method ◽  
Information Criteria ◽  
Series Data ◽  
Percentage Error ◽  
Model Forecast ◽  
Modeling And Forecasting ◽  
Periodic Autocorrelation Function

Most frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagnosed to be white noise. The in-sample forecast showed a close reflection of the original rainfall series while the out-sample forecast exhibited a continuous periodic forecast from January 2019 to December 2020 with relatively small values of Periodic Root Mean Square Error (PRMSE), Periodic Mean Absolute Error (PMAE) and Periodic Mean Absolute Percentage Error (PMAPE). The comparison of FAR(1) model forecast with AR(3), ARMA(2,1), ARIMA(2,1,1) and SARIMA( 1,1,1)(1,1,1)12 model forecast indicated that FAR(1) outperformed the other models as it exhibited a continuous periodic forecast. The continuous monthly periodic rainfall forecast indicated that there will be rapid climate change in Nigeria in the coming yearly and Nigerian Government needs to put in place plans to curtail its effects.

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

scholarly journals Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019 ◽  
Vol 11 (3) ◽  
pp. 793 ◽  
Author(s):  
Rashad Aliyev ◽  
Sara Salehi ◽  
Rafig Aliyev
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

scholarly journals Unsupervised Classification During Time-Series Model Building

2016 ◽  
Vol 52 (2) ◽  
pp. 129-148 ◽  
Author(s):  
Kathleen M. Gates ◽  
Stephanie T. Lane ◽  
E. Varangis ◽  
K. Giovanello ◽  
K. Guiskewicz
Keyword(s):  
Time Series ◽  
Model Building ◽  
Unsupervised Classification ◽  
Time Series Model

scholarly journals Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e018628 ◽  
Author(s):  
Wang-Chuan Juang ◽  
Sin-Jhih Huang ◽  
Fong-Dee Huang ◽  
Pei-Wen Cheng ◽  
Shue-Ren Wann
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Statistical Tests ◽  
Demand Forecasting ◽  
Information Criterion ◽  
Emergency Department Visits ◽  
Percentage Error ◽  
Initial Development ◽  
Adequate Model ◽  
Ed Visits

ObjectiveEmergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.MethodsWe retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.ResultsA series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1).ConclusionThe ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.

scholarly journals Automation of Best-Fit Model Selection using a Bag of Machine Learning Libraries for Sales Forecasting

2021 ◽  
Vol 12 (06) ◽  
pp. 17-26
Author(s):  
Pauline Sherly Jeba P ◽  
Manju Kiran ◽  
Amit Kumar Sharma ◽  
Divakar Venkatesh
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Automotive Industry ◽  
Model Building ◽  
Percentage Error ◽  
Business Plans ◽  
Customer Group ◽  
Exploratory Data ◽  
One Year ◽  
Best Fit

Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.

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