scholarly journals Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1848 ◽  
Author(s):  
Yuehjen Shao ◽  
Yi-Shan Tsai
Keyword(s):  
Moving Average ◽  
Multivariate Adaptive Regression Splines ◽  
Predictor Variables ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Explanatory Variables ◽  
Forecasting Models ◽  
Hybrid Approaches ◽  
The Future ◽  
Hybrid Forecasting

Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

scholarly journals Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuehjen E. Shao
Keyword(s):  
Body Fat ◽  
Hybrid Models ◽  
The Body ◽  
Single Stage ◽  
Support Vector ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Explanatory Variables ◽  
Forecasting Models ◽  
Hybrid Approaches

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.

scholarly journals Forecasting Sugarcane yield of India using ARIMA-ANN hybrid model

2018 ◽  
Vol 33 (01) ◽  
Author(s):  
Mrinmoy Ray ◽  
R. S. Tomar ◽  
Ramasubramanian V. ◽  
K. N. Singh
Keyword(s):  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Main Stream ◽  
Research Activity ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Cash Crops ◽  
Sugarcane Yield ◽  
The Individual

Sugarcane is one of the main cash crops of India hence forecasting sugarcane yield is vital for proper planning. Till date Autoregressive integrated moving average (ARIMA) model is a stand out amongst the most main stream approach for sugarcane yield forecasting. Recent research activity reveals that hybrid model improves the accuracy of forecasting when contrasted with the individual model. Along these lines, in this study, ARIMA-ANN hybrid model was utilized for forecasting sugarcane yield of India. The hybrid model was compared with ARIMA approach. Empirical results clearly reveal that the forecasting accuracy of the hybrid model is superior to ARIMA.

scholarly journals Forecasting Techniques for Sales Prediction

2020 ◽  
Vol 8 (5) ◽  
pp. 4924-4927
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Fuel Price ◽  
Common People ◽  
Forecasting Models ◽  
Sales Prediction ◽  
The Future ◽  
Feed Forward Neural Networks ◽  
Past Data ◽  
Predicted Values

All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales - markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.

scholarly journals The influence of climate factors on COVID-19 transmission in Malaysia: An autoregressive integrated moving average (ARIMA) model

2020 ◽  
Author(s):  
Cho Naing ◽  
Han Ni ◽  
Htar Htar Aung ◽  
Elaine Chan Wan Ling ◽  
Joon Wah Mak
Keyword(s):  
Moving Average ◽  
Descriptive Analysis ◽  
Arima Model ◽  
State Level ◽  
Climate Factors ◽  
Single Digit ◽  
Autoregressive Integrated Moving Average ◽  
Explanatory Variables ◽  
Average Temperature ◽  
Climate Parameters

Background A unique concern pertaining to the spread of COVID-19 across countries is the asymmetry of risk and the irrational fear of a new pandemic and its possible serious consequences. This study aimed to perform a time series analysis on the association between climate factors and daily cases of COVID-19 in Malaysia up to 15 July 2020. The second objective was to predict daily new cases using a forecasting technique. To address within-country variations, the analysis was extended to the state level with Sarawak state as an example. Methodology/Principal Findings Datasets on the daily confirmed cases and climate variables in Malaysia and Sarawak state were obtained from publicly accessible official websites. A descriptive analysis was performed to characterize all the important variables over the study period. An autoregressive integrated moving average (ARIMA) model was introduced using daily cases as the dependent variable and climate parameters as the explanatory variables. For Malaysia, the findings suggest that, ceteris paribus, the number of COVID-19 cases decreased with increasing average temperature (p=0.003) or wind speed (p=0.029). However, none of the climate parameters showed a significant relationship with the number of COVID-19 cases in Sarawak state. Forecasts from the ARIMA models showed that new daily COVID-19 cases had already reached the outbreak level and a decreasing trend in both settings. Holding other parameters constant, a small number of new cases (approximately a single digit) is a probable second wave in Sarawak state, Conclusions/Significance The findings suggest that climate parameters and forecasts are helpful for reducing the uncertainty in the severity of future COVID-19 transmission. A highlight is that forecasts will be a useful tool for making decisions and taking the appropriate interventions to contain the spread of the virus in the community.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

2017 ◽  
Vol 8 (4) ◽  
pp. 30-53 ◽  
Author(s):  
Warut Pannakkong ◽  
Van-Hai Pham ◽  
Van-Nam Huynh
Keyword(s):  
Hybrid Model ◽  
Forecast Error ◽  
Moving Average ◽  
Data Sets ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Hybrid Forecasting ◽  
Us Dollar ◽  
Different Levels ◽  
Prediction Capability

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

scholarly journals Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Jun-Ting Dai
Keyword(s):  
Feature Selection ◽  
Computational Intelligence ◽  
Integrated Model ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Food Crop ◽  
Explanatory Variables ◽  
Price Prediction ◽  
Forecasting Models ◽  
Major Food

Because of global climate change, lack of arable land, and rapid population growth, the supplies of three major food crops (i.e., rice, wheat, and corn) have been gradually decreasing worldwide. The rapid increase in demand for food has contributed to a continuous rise in food prices, which directly threatens the lives of over 800 million people around the world who are reported to be chronically undernourished. Consequently, food crop price prediction has attracted considerable attention in recent years. Recent integrated forecasting models have developed various feature selection methods (FSMs) to capture fewer, but more important, explanatory variables. However, one major problem is that the future values of these important explanatory variables are not available. Thus, predictions based on these variables are not actually possible. Because an autoregressive integrated moving average (ARIMA) can extract important self-predictor variables with future values that can be calculated, this study incorporates an ARIMA as the FSM for computational intelligence (CI) models to predict three major food crop (i.e., rice, wheat, and corn) prices. Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). The predictive accuracies of ARIMA, ANN, SVR, MARS, and the proposed integrated model are compared and discussed. Experimental results reveal that the proposed integrated model achieves superior forecasting performance for predicting food crop prices.

PERBANDINGAN ARIMA DAN ARTIFICIAL NEURAL NETWORKS DALAM PERAMALAN JUMLAH POSITIF COVID-19 DI DKI JAKARTA

2021 ◽  
Vol 10 (3) ◽  
pp. 288-301
Author(s):  
Tri Wahyuni ◽  
Indahwati Indahwati ◽  
Kusman Sadik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Moving Average ◽  
Percentage Error ◽  
Cumulative Number ◽  
Mean Square ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural

DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks.

Sign in / Sign up

Export Citation Format

Share Document