scholarly journals Estimating missing data in historic series of global radiation through neural network algorithms

2016 ◽  
Vol 14 (37) ◽  
pp. 9-22
Author(s):  
Franklin García Acevedo ◽  
Juan Rojas Serrano ◽  
Alejandro Vásquez Vega ◽  
Diego Parra Peñaranda ◽  
Erney Castro Becerra
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Missing Data ◽  
Meteorological Data ◽  
Moving Average ◽  
Global Radiation ◽  
Network Algorithms ◽  
Autoregressive Integrated Moving Average ◽  
Recorded Data ◽  
Temperature Radiation

In data processing time series of meteorological data problems, you are incomplete in some time intervals; it addresses the issue commonly using the autoregressive integrated moving average (ARIMA) or the method by regression analysis (interpolation), both with certain limitations under particular conditions. This paper presents the results of an investigation aimed at solving the problem using neural networks reported. The analysis of a time series of global radiation obtained at the Francisco de Paula Santander University (UFPS) is presented, with basis in the recorded data by the weather station attached to the Department of Fluids and Thermals. Having a series of ten-year study for 125,658 records of temperature, radiation and energy with a percentage of 9.98 missing data, which were duly cleared and completed by a neural network using algorithms backpropagation in the mathematical software MATLAB

scholarly journals Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel Adedayo Adeyinka ◽  
Nazeem Muhajarine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mortality Rates ◽  
Childhood Mortality ◽  
Autoregressive Integrated Moving Average ◽  
Under Five ◽  
Deming Regression

Abstract Background Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods—ARIMA regression and Holt-Winters exponential smoothing models. Methods The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression. Results The modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference = 0.11, p-value = 0.0003), and Holt-Winters model (difference = 0.62, p-value< 0.001). Based on the intercepts from Deming regression, the predictions from GMDH-type ANN were more accurate (β0 = 0.004 ± standard error: 0.06; 95% confidence interval: − 0.113 to 0.122). Conclusions GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.

scholarly journals COMPARISON BETWEEN HYBRID QUANTILE REGRESSION NEURAL NETWORK AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS VARIABLE FOR FORECASTING OF CURRENCY INFLOW AND OUTFLOW IN BANK INDONESIA

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Dedy Dwi Prastyo ◽  
Suhartono Suhartono ◽  
Agnes Ona Bliti Puka ◽  
Muhammad Hisyam Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Quantile Regression ◽  
Empirical Result ◽  
Moving Average ◽  
Exogenous Variable ◽  
Real Case ◽  
Autoregressive Integrated Moving Average ◽  
Series Analysis

Some problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are still limited. Therefore, this study performed a comparison between hybrid Quantile Regression Neural Network (QRNN) and Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX). Both methods were employed to model the currency inflow and outflow from Bank Indonesia in Nusa Tenggara Timur province. Based on the empirical result, the hybrid QRNN method provided better forecasting for currency outflow whereas the ARIMAX resulted in better forecasting for the inflow. 

scholarly journals Time-series analysis of tuberculosis from 2005 to 2017 in China

2018 ◽  
Vol 146 (8) ◽  
pp. 935-939 ◽  
Author(s):  
H. Wang ◽  
C. W. Tian ◽  
W. M. Wang ◽  
X. M. Luo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Notification Rate ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Combination Model ◽  
Series Analysis ◽  
The Mean

AbstractSeasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA–GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA–GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA–GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

scholarly journals Analyzing and Forecasting of Electricity Consumption by Integration of Autoregressive Integrated Moving Average Model with Neural Network on Smart Meter Data

2021 ◽  
Vol 12 (11) ◽  
pp. 1986-1997
Author(s):  
M. Suresh, Et. al.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Electricity Consumption ◽  
Energy Utilization ◽  
Superior Performance ◽  
Series Data ◽  
Smart Meter ◽  
Autoregressive Integrated Moving Average

Smart metering is a recently developed research area over the globe and it appears to be a remedy for increasing prices of electricity. Electricity consumption forecasting is an essential process in offering intelligence to smart girds. Rapid and precise forecasting allows a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will advantage from the metering solutions by a greater understanding of their own energy utilization and forthcoming projections, allowing them to effectively manage the cost of their consumption. In this view, this paper presents an Integration of Autoregressive Integrated Moving Average (ARIMA) Model with Neural Network (NN) for Electricity Consumption Forecasting using Smart Meter Data. As the time series data often does not hold linear as well as nonlinear patterns, ARIMA or NN models are not enough to model and predict the time series data. The ARIMA-NN model will be trained using the data and generates a model. Afterward, the generated model can be utilized to predict the electricity consumption by the application of new building data. The proposed ARIMA-NN model is evaluated and the simulation outcome strongly pointed out its superior performance over the compared methods. The presented model has obtained effective testing performance with the MAPE of 25.53, an accuracy of 48.38, and MSE of 0.21.

scholarly journals Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Reihaneh Bandari ◽  
Mohammad Valipour
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Peak Flow ◽  
Moving Average ◽  
Flow Time ◽  
Autoregressive Integrated Moving Average ◽  
Auto Regressive ◽  
Reservoir Inflow

In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average
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