scholarly journals A Comparative Study of Fourier Series Models and Seasonal -Autoregressive Integrated Moving Average Model of Rainfall Data in Port Harcourt

2021 ◽  
pp. 36-46
Author(s):  
Wiri Leneenadogo ◽  
Sibeate Pius U
Keyword(s):  
Fourier Series ◽  
Moving Average ◽  
Information Criterion ◽  
Rainfall Data ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Port Harcourt ◽  
Moving Average Model ◽  
Rivers State ◽  
Time Plot

This study compares the Seasonal autoregressive integrated moving average (SARIMA) model within Fourier time series model in modelling rainfall data in Port Harcourt Rivers State from 2000-2014. The time plot of the series showed Seasonality but a not obvious trend. The raw data is nonstationary at the level. Time plot of the seasonal differencing of rainfall at lag12 showed a stationary process with seasonality at lag 12 on the PACF and ACF of the series. The periodogram plot reveals that there exist both short and long term cycles within the period. The Fourier series and the seasonal autoregressive moving average models are reduced to 12month of seasonal component.( ) The Akaike Information Criterion (AIC) was used to select better models. The best model is the model that minimises the information criterion. It was observed that SARIMA (1,0,1)(1,1,1)12 models have a minimum AIC value. Hence, SARIMA model performs better in modelling the rainfall data in Port Harcourt then the Fourier series models.

scholarly journals Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model

2021 ◽  
pp. 22-35
Author(s):  
Dmytro Chumachenko ◽  
Ievgen Meniailov ◽  
Andrii Hrimov ◽  
Vladislav Lopatka ◽  
Olha Moroz ◽  
...  
Keyword(s):  
Process Simulation ◽  
Respiratory Infections ◽  
Moving Average ◽  
Simulation Models ◽  
Influenza Epidemic ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Epidemic Process ◽  
Moving Average Model ◽  
Control And Prevention

Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.

scholarly journals Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Dwi Asa Verano ◽  
Husnawati Husnawati ◽  
Ermatita Ermatita
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Raw Material ◽  
Digital Printing ◽  
Printing Industry ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

scholarly journals Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Didin Muhjidin ◽  
Tedjo Sukmono
Keyword(s):  
Moving Average ◽  
Market Demand ◽  
Average Model ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Time Series ◽  
Moving Average Model ◽  
Make To Stock ◽  
Market Needs ◽  
Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

scholarly journals Modelling the Nigeria Crude Oil Prices Using ARIMA, Pre-intervention and Post-intervention Model

2019 ◽  
pp. 1-12
Author(s):  
Wiri, Leneenadogo ◽  
Tuaneh, Godwin Lebari
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Oil Prices ◽  
Information Criterion ◽  
Intervention Model ◽  
Post Intervention ◽  
Autoregressive Integrated Moving Average ◽  
Crude Oil Prices ◽  
Augmented Dickey Fuller ◽  
Time Plot

The study applied Autoregressive Integrated Moving Average Intervention in modelling crude oil prices in Nigeria spanning the period from January 1986 to June 2017. The time plot of the series showed an abrupt increase in the series and this called for intervention modelling. The data was divided into three set (actual series, pre-intervention and post-intervention series). The Augmented Dickey Fuller (ADF) was used to test for unit root on each of the series and were all found to be non-stationary at levels, they (actual, pre and post- intervention series) were however non stationary at first difference. Eighteen models were estimated and the best model was the pre-intervention model that minimise the Akaike information criterion (AIC) (ARIMA (111)) with AIC of (4.4.578). The plot of the residual correlogram showed adequacy of the model. The model was adequate since there was no spike that cut the level of the correlogram and the histogram of the residual was normally distributed with probability values (0.0000).

scholarly journals Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

scholarly journals When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility

Econometrica ◽  
2021 ◽  
Vol 89 (6) ◽  
pp. 2787-2825 ◽  
Author(s):  
Rui Da ◽  
Dacheng Xiu
Keyword(s):  
High Frequency ◽  
Moving Average ◽  
Information Criterion ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Finite Samples ◽  
Moving Average Models ◽  
Moving Average Model ◽  
Itô Semimartingale

We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. We show that the convergence rate of our estimator dominates n 1/4 as noise vanishes, and is determined by the selected order of noise dependence when noise is sufficiently small. Our implementation guarantees positive estimates in finite samples.

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