Linear ARIMA Model Research Articles

US Gasoline Prices: Linear Regression Model and ARIMA Model Forecast

US Gasoline Prices: Linear Regression Model and ARIMA Model Forecast

Research on Vegetable Product Pricing and Replenishment Decision Based on Linear Regression and ARIMA Model

With the rise of fresh food supermarkets, how to automatically price and restock vegetable products has become a major challenge for many businesses. To develop a restocking and pricing plan for vegetable products in supermarkets, we first calculate the cost profit margin, standardize the profit margin and total sales volume data, and then incorporate vegetable categories into a linear regression model using dummy variables, We found a significant negative correlation between profit margin and total sales volume, and obtained the product pricing strategy using the linear equation of profit margin. After conducting ADF testing, the number of autoregressive terms and the average number of sliding terms of the Time Series (ARIMA) model were determined based on the test set results, and the model was used to predict the total daily replenishment of each vegetable category in the next week. This study has reference value for vegetable restocking and pricing decisions.

Analysis on the Time Series Forecasting on COVID-19 New Infections Declining in Shanghai, China

This research focus on the forecast of the effect of policy influence on COVID-19 in Shanghai, China, during March to May in 2022. By using new infection numbers in 50 days as the train model, four different models were used for forecasting the new infection numbers in future 14 days. Data was published on China Centers for Disease Control and Prevention. Simple exponential smoothing, Holt’s linear method, ARIMA model and ETS model were utilized and compared the accuracy with each other to find the best fit forecast model. As the result, the ARIMA (2,2,2,) model best fits the decline of the new infection. This result needs a condition of similar infectiousness to COVID-19 and the policies and efforts similar to those used to control the outbreak in Shanghai from March to May 2022. This research helps the forecast for policy effects in future epidemiologic disease control.

Assessing How COVID-19 Pandemic Affects the Tourism Industry in Mainland China

The study primarily contends that the rise in COVID-19 cases has adversely impacted the volume of tourist visits. This investigation focuses on how the pandemic affected the hotel sector during the outbreak. Throughout the entire economy, the hospitality sector may be among the areas most significantly impacted by the pandemic. Hence, employing linear regression and ARIMA models, this investigation looks at the relationship between the number of COVID-19 instances. and tourism expenditure, overall spending on star-rated hotels, and per capita GDP in mainland China. These factors can represent tourists budgets and wiliness to spend during the pandemic. Furthermore, the research also considers whole-city quarantine and health codes to show the restriction when tourists travel during the pandemic. According to the test findings, the research reveals that a 1% rise in COVID-19 cases correlates with an estimated 0.108% reduction in tourist numbers.

Wind Power Deviation Charge Reduction using Machine Learning

High penetration of wind power plants in power systems resulted in various challenges such as frequent system imbalances due to highly uncertain and variable wind generation. Additional spinning reserves and specific balancing products such as flexible ramp products are used to handle such frequent imbalances. Incorporation of these ancillary services leads to increased total operational costs. Increased operational costs should be transferred to wind power producers as it is caused by wind power plants. This leads to penalizing the wind power producers for the deviation of power generation from forecasts, called deviation charges. These deviation charges can be reduced by improving the forecasting accuracy. Existing forecasting models show performance in terms of error matrices. Such error matrices do not indicate the financial loss associated with it. This can be overcome by expressing forecasting performance in terms of deviation charge and it will directly encourage wind power producers to improve forecasting accuracy or arrange reserves to accommodate the error. This paper proposes a backpropagation-based artificial neural network model for reducing deviation charges in this context. An analysis is conducted on the data collected from the Bonneville Power Administration (BPA) Balancing Area. Seasonal analysis (Spring, Summer, Fall, and Winter) is conducted to show the performance of the proposed model throughout the year. The proposed model performance is compared with linear regression and ARIMA models. The comparison shows that the proposed ANN model gives the least deviation charges in the Spring, Summer, and Winter seasons and deviation charges in the Fall season are higher than the ARIMA model.

A Comparison of Linear Regression, LSTM model and ARIMA model in Predicting Stock Price A Case Study: HSBC’s Stock Price

It is widely assumed that the stock market is a key aspect of the financial market and stock price forecasting has become a popular topic of an in-depth study by financial technologists. With the advent of financial markets, stock prices face several analytical and forecasting challenges. In this paper, the stock price trend forecast analysis is carried out and the survey object was the transaction data of HSBC from 2010 to 2019. Linear regression, LSTM model and ARIMA model are used to forecast stock price trends. The models’ prediction accuracy is demonstrated after the cross-validation process by combining error indicators and trading performance. As a consequence of the analysis, we discover that LSTM has the lowest error value, which implies that it is ideally suited to stock market forecasting. On the contrary, linear regression holds the largest mean square error, implying that this method has the weakest fitting for the stock market.

Sales Prediction Based on ARIMA Time Series and Multifactorial Linear Model

Contemporarily, sales forecasting based on historical data is one of the key topics of research. Researchers have found that different analytical forecasting models can predict the future sales volume to a certain extent based on different actual conditions. However, it is also very important to compare the prediction results of these models, and a reasonable choice of models can help managers to make more scientific decisions. Therefore, in this paper, the historical sales data of a store on the Kaggle is utilized to make predictions based on different models, and investigate the impact of linear regression model and time series model on sales prediction. In this paper, multiple factorial linear regression model and ARIMA model in time series analysis will be adopted based on R to build prediction models to forecast sales volume, respectively. According to the analysis, the root mean square error of the prediction results under the multiple factorial linear regression model and the ARIMA model were 21.135 and 23.221, respectively. The study surface that there were differences in the degree of fit of the prediction results by different pairs of models. Therefore, the choice of the researcher needs to select the appropriate model for prediction according to the specific object of analysis, combined with the data characteristics to achieve the expected results. These results shed on guiding further exploration of differences in predictions from different models.

Forecasting The Composite Stock Price Index Using Autoregressive Integrated Moving Average Hybrid Model Artificial Neural Network

A stock index is a statistical measure that reflects the overall price movement of a group of stocks selected based on certain criteria and methodologies and evaluated regularly. JCI is included in the composite index, which is the Headline index. The Headline Index is an index that is used as the main reference to describe the performance of the capital market. The JCI is very important in describing the current condition of the capital market because the JCI measures the price performance of all stocks listed on the Main Board and Development Board of the IDX. This study aims to predict JCI data using the time series method. The hybrid Autoregressive Integrated Moving Average–Artificial Neural Network (ARIMA-ANN) model combines the linear ARIMA model and the non-linear ANN model. The best models are the ARIMA model (2,1,1) and the ANN Backpropagation model with one input layer, one hidden layer with 20 neurons, and one output. The ARIMA-ANN hybrid model accurately predicts JCI data because it produces a MAPE value of less than 1%, with the level of forecasting accuracy from testing results being smaller than the level of accuracy during training. In addition, the forecast for the next five days is very accurate because it produces a very small RMSE and a MAPE below 1%, respectively, namely 56.99 and 0.72%.

A Comparative Study of Autoregressive Integrated Moving Average and Artificial Neural Networks Models

In this study, the forecasting capabilities of nonlinear models as Artificial Neural Networks and linear ARIMA models were compared. The comparison was conducted using the daily data of Nigeria’s All Share Index for 11 years. The empirical findings revealed ARIMA (1,1,2) model as the best fit for Nigeria’s All Share Index among other Box Jenkins models. This was supported by the most fit statistic test. Also, ANN model with three units in the hidden layer, two lags and the learning rate equal to 0.1, returned as the best fit for the Nigeria All Share Index forecasting. Furthermore, while comparing the performance of the two models, the RMSE of the ARIMA model equivalent to 0.0136 is higher than the RMSE of the ANN model (0.0048), indicating the efficiency of the ANN model. Thus, we can conclude from the above statistics that the ANN model is more efficient. As a result, the study recommends taking advantage of the high capacity of artificial neural networks as a forecasting technique in other fields, such as medical research, genetics research, industrial research, energy, and military research.

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

This article focuses on the predictive study of electricity demand in West African countries based on the multivariate linear regression model and the ARIMA model. The objective of the study is first to establish for each country a linear regression model and ARIMA model, then to compare the two (2) models based on the MAD, RMSE and MAPE coefficients, and finally to deduce of this comparison the best valid model to establish the electricity demand prediction of the country. We have come to the conclusion that the ARIMA model is more adequate for predicting the electricity demand of most of West African countries with the exception of Gambia, Ghana, Guinea, Liberia and Nigeria where the multivariate linear regression model performs better.

Analysis And Prediction of Influencing Factors of Total Retail Sales of Consumer Goods in China——Based on Multiple Linear Regression Models and ARIMA Models

This paper takes the annual data of total retail sales of consumer goods in China from 2001 to 2020 as the research object. Using methods such as principal component estimation, ridge regression and Lasso regression, a multivariate linear regression model is established, and the regression results of the three models are compared and analyzed, and the significant factors affecting the total retail sales of consumer goods in China are the income level of residents, the level of consumption of residents, and the development of the tertiary industry, and the less significant factors are demographic factors. In addition, this paper takes the annual data of china's total retail sales of social consumer goods from 1973 to 2020 as the research object, and uses the ALAMA (0,1,1) model to predict and analyze the total retail sales of social consumer goods in China in the next ten years, and concludes that the total retail sales of social consumer goods in China will gradually rise from 2021 to 2030, which is consistent with the actual situation. Thus, it puts forward rationalization suggestions for increasing the total retail sales of consumer goods in China and promoting economic development.

GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data

In order to promote the economic development of China’s provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China’s provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction.

Comparison of Time Series Models before and after Using Wavelet Shrinkage Filtering to Forecast the Amount of Natural Gas in Iraq

The procedure of reducing noise or reduction before analyzing the time series is important to get accurate and reliable outcomes when building models. Wavelet Shrinkage consisting of wavelets with thresholding is a powerful mathematical method used to reduce noise that can be exposed. It has time-series observations and selects the cut-off threshold level to be suitable for removing most noise. In this paper, natural gas production data in Iraq were used during (1981-2019), which included non-stationary data or outliers, so the researcher must treat this problem before starting the analysis. Thus, the researcher selected the way of time series analysis using Box- Jenkins (ARIMA (p,d,q) models and the method of wavelet shrinkage in the two methods of cutting the threshold before and after filtering wavelet shrinkage to choose the most suitable mathematical model that defines the study data. The paper was concerned with finding an efficient model by comparing the (Box-Jenkins) linear ARIMA (p, d, q) models estimated from the time series data before and after filtering the wavelet shrinkage, and then decreasing the rank of the estimated model from the candidate observations (While maintaining the accuracy and suitability of the estimated models) and re-comparing it with the estimated linear models of the original observations, and then measuring the most efficient model based on some statistical criteria, including the AIC (Akaike information criterion), SBIC (Schwarz information criterion, Bayesian information criterion BIC)and HQIC (Hannan-Quinn information criterion). Statistical programs such as Statgraphics XVII - X64 and MATLAB were used to analyze the data. The paper reached the efficiency of wavelet shrinkage filters in treating the noise problem and getting efficient estimated models, specifically the wavelet shrinkage filter (Daubechies(db8)) with a soft-threshold cut-off estimated by the fixed-form method, and the possibility of obtaining linear models with lower orders and higher efficiency for the filtered observations compared to the corresponding models estimated from the original observations, that is, the suitable model for the data is a model ARIMA(0,1,1) for the data paved in a Daubechies method, i.e., it is preferable to do the waveform analysis of the data before analyzing the time series concerning the observations of natural gas production in Iraq The preference was given to the model estimated by Box and Jenkins ARIMA (0,1,1) model using wavelet shrinkage. Daubechies (db8) wavelet reduction

Modeling and forecasting of milk production in different breeds in Turkey

Identification of milk production is one of the key activities for the Turkish economy in terms of providing economic income for dairy smallholders and meeting consumer demands. This study aims to predict milk production in Turkey using various time series models which are BATS, TBATS, Holt's Linear Trend, ARIMA models, and NNAR. Yearly data from 1991 to 2019 on Milk Production is used in this study. The best time series model is selected from the testing data set (2015 to 2019) based on the lowest Mean Absolute Percentage Error (MAPE). The results of this study showed that the best predicts are obtained for Culture purebred milk production by an ARIMA (1,2,1) model, for Crossbreed milk production by a TBATS Model, for Indigenous milk production by ARIMA (0,2,0) model for total milking cows' production by Holt’s Linear Trend model. Furthermore, these models forecasted an increasing trend in the production of milk from 2020 to 2025. The percentage increases for culture purebred milk, Crossbreed milk production, and Indigenous milk production from 2020 to 2025 are projected to be 40, 20.9 and 10.9%, respectively. Overall, the total milking cows' production is projected to increase by 25% in 2025.

Comparative Performance of Simple Exponential Smoothing, Brown’s Linear Trend and ARIMA Model on Forecasting Neonatal Mortality Rate in Nigeria

Paper proposes an appropriate time series model that is used to forecast the NMR in Nigeria. The data used for the study is sourced from the World Bank for a period of 1980-2019. The ARIMA model and Exponential Smoothing are fitted on the raw data. The Bayesian Information Criterion (BIC) is adopted to assess the adequacy of the ARIMA models. The NMR series is stationary after the second differencing. The ARIMA (0,2,0) with BIC value of -3.358 is considered the appropriate model among other ARIMA models, and it is compared to SES and Brown’s LT using Theil’s U Statistics and MAPE. The results showed that the Brown’s LT model is more ideal and adequate for forecasting NMR in Nigeria based on the Theil’s U forecast accuracy measures of 0.001911, and that by 2030, Nigeria will have a reduced NMR of 31.5 deaths per 1,000 live births, which shows a drop to 21.5%.

Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis

BackgroundWe investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across five London hospitals.MethodsA retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across five acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation.ResultsOne hundred nineteen thousand five hundred eighty-four blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst all CoNS BSI were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p = 0.013), CoNS central line associated BSIs (CLABSI) (p < 0.01) and CoNS non-CLABSI (p < 0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p = 0.631) and S. aureus (p = 0.617) BSI did not vary significant throughout the study period.ConclusionsSignificantly fewer than expected Enterobacterales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, with evidence of increased CLABSI, but also likely contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.

A Statistical Analysis of Drug Seizures and Opioid Overdose Deaths in Ohio from 2014 to 2018

Objective. To examine the association between drug seizures and drug overdose deaths in Ohio from 2014 to 2018.&#x0D; Methods. We use linear regression, ARIMA models, and categorical data analysis to quantify the effect of drug seizure composition and weight on drug overdose deaths, to quantify the lag between drug seizures and overdose deaths, and to compare the weight distributions of drug seizures conducted by different types of law enforcement (national, local, and drug task forces).&#x0D; Results. Drug seizure composition and weight have strong predictive value for drug overdose deaths (F = 27.14, p &lt; 1e-15, R² = .7799). A time series analysis demonstrates no statistically significant lag between drug seizures and overdose deaths or weight. Histograms and Kolmogorov-Smirnov tests demonstrate stark differences between seizure weight distributions of different types of law enforcement (p &lt; 1e-7 for each pairwise comparison).&#x0D; Conclusions. Consideration of drug composition and weight can inform law enforcement seizure activity. To save lives, law enforcement should emphasize seizures of low weight drugs that contain fentanyl.

Forecasting influenza-like illness trends in Cameroon using Google Search Data

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

Empirical Investigation on COVID-19 Cases Using Machine Learning Approach: State Wise Analysis of India

This COVID-19 pandemic gives the opportunities to scientists for their big researches and covid-19 become as a challenge for scientific communities to foster their Researches and Development activities. This paper presents a comparative analysis of machine learning model to predict the COVID-19 outbreaks. We analyze the most affected states which the helps of line graphs and show the number of deaths, number of recoveries and total number of confirmed cases of that states This paper builds the predictive models that can predict the upcoming number of confirmed cases, upcoming number of deaths and future recoveries for next 90 days with the higher accuracy We used several machine learning algorithms including linear regression, Random Forest, Decision Tree and ARIMA model for forecasting. analyze the most affected states which the helps of line graphs and show the number of deaths, number of recoveries and total number of confirmed cases of that states.