scholarly journals Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters

2010 ◽  
Vol 6 (S273) ◽  
pp. 446-450 ◽  
Author(s):  
Yuan Yuan ◽  
Frank Y. Shih ◽  
Ju Jing ◽  
Haimin Wang
Keyword(s):  
Solar Flare ◽  
Neutral Line ◽  
Forecasting Accuracy ◽  
Magnetic Parameters ◽  
Forecasting Method ◽  
Total Gradient ◽  
Strong Gradient ◽  
Classification Information ◽  
Year 2000 ◽  
Sunspot Groups

AbstractIn this paper, we investigate whether incorporating sunspot-groups classification information would further improve the performance of our previous logistic regression based solar flare forecasting method, which uses only line-of-sight photospheric magnetic parameters. A dataset containing 4913 samples from the year 2000 to 2005 is constructed, in which 2721 samples from the year 2000, 2002 and 2004 are used as a training set, and the remaining 2192 samples from the year 2001, 2003 and 2005 are used as a testing set. Experimental results show that sunspot-groups classification combined with total gradient on the strong gradient polarity neutral line achieve the highest forecasting accuracy and thus it testifies sunspot-groups classification does help in solar flare forecasting.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

Comparison Analysis of Aircraft Aluminum Alloy Corrosion Damage Forecasting Method

2012 ◽  
Vol 268-270 ◽  
pp. 348-351
Author(s):  
Zhi Guo Liu ◽  
Zhi Tao Mu ◽  
Zeng Jie Cai
Keyword(s):  
Time Series ◽  
Aluminum Alloy ◽  
Corrosion Damage ◽  
Neural Net ◽  
Series Method ◽  
Comparison Analysis ◽  
Forecasting Accuracy ◽  
Fitting Method ◽  
Forecasting Method ◽  
Time Series Method

Three different analysis methods was put forward to carried out aircraft aluminum alloy structure corrosion damage forecasting,and comparison analysis of different method which included basic forecasting caculation principle and forecasting accuracy and forecasting extensionality also was discussed.The forecasting calculation result shows that the prediction accuracy of neural net and time series method is higher than the data fitting method,and the prediction extensionality of time series method is the best among the three method which discussed.

scholarly journals PERANCANGAN MODEL UNTUK PERAMALAN TINGKAT PERTUMBUHAN PEMBIAYAAN MELALUI METODE JARINGAN SARAF TIRUAN DI BNI SYARIAH

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Orfyanny S Themba ◽  
Susianah Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Data Bank ◽  
Forecasting Accuracy ◽  
The Neural Network ◽  
Forecasting Method ◽  
Artificial Neural

ABSTRAKTren perkembangan pembiayaan di Indonesia mulai meningkat namun cenderung melambat dari tahun ke tahun. Peramalan pertumbuhan pembiayaan pada bank syariah menjadi hal yang menarik karena naik turunnya pembiayaan akan berdampak pada perekonomian Indonesia. Tujuan dari penelitian ini melakukan peramalan pertumbuhan pembiayaan dalam jangka waktu setahun melalui metode Jaringan Saraf Tiruan pada data Bank BNI Syariah dari tahun 2015 sampai dengan 2019. Hasil dari peramalan diharapkan memberi informasi bagi bank untuk menunjang pengambilan keputusan dan menyiapkan strategi meningkatkan pembiayaan sehingga semakin besar laba yang akan diperoleh. Model peramalan dibuat berdasarkan metode peramalan dan ditujukan untuk digunakan pada aplikasi peramalan pembiayaan. Model Jaringan Saraf Tiruan memiliki nilai akurasi peramalan yang tinggi karena memiliki nilai error RMSE, MAPE yang minimum. Dari hasil peramalan menggunakan model Jaringan Saraf Tiruan menunjukkan terjadi peningkatan pembiayaan pada setiap bulannya untuk akad murabahah, mudharabah, musyarakah dan qardh. Hanya pembiayaan yang menggunakan ijarah yang mengalami penurunan drastis dibanding tahun-tahun sebelumnya. Pembiayaan murabahah masih tetap mendominasi dibanding akad mudharabah, musyarakah, qardh dan ijarah selama tahun 2020 Kata Kunci: Jaringan Saraf Tiruan ;PembiayaanABSTRACT Trend of financing development in Indonesia is starting to increase but tends to slow down from year to year. It is interesting to forecast the growth of financing in Islamic banks because the up and down of financing will have an impact on the Indonesian economy. The purpose of this study to forecast financing growth within a year through the Neural Network method on BNI Syariah Bank data from 2015 to 2019. The results of the forecast are expected to provide information for banks to support decision making and prepare strategies to increase financing so that greater profits that will be obtained. The forecasting model is made based on the forecasting method and is intended for use in financing forecasting applications. The Artificial Neural Network Model has a high value of forecasting accuracy because it has a minimum error value of RMSE, MAPE. The results of forecasting using the Artificial Neural Network model show an increase in financing every month for murabahah, mudharabah, musyarakah and qardh contracts. Only financing using ijarah has experienced a drastic decline compared to previous years. Murabahah financing still dominates over the mudharabah, musyarakah, qardh and ijarah contracts during 2020Keyword: Arificial Neural Network ;Financing

Improvement of Forecasting Method of Recession Characteristics of River Flow Rate into a Dam by using Estimation of Steady State

2020 ◽  
pp. 1-10
Author(s):  
Tomonari Kawai ◽  
Katsuhiro Ichiyanagi ◽  
Takuo Koyasu ◽  
Kazuto Yukita ◽  
Yasuyuki Goto
Keyword(s):  
Steady State ◽  
State Estimation ◽  
Flow Rate ◽  
River Flow ◽  
System Operation ◽  
Forecasting Accuracy ◽  
Hydropower Plants ◽  
Dam Management ◽  
Forecasting Method

This paper describes an application of neural networks for forecasting the flow rate upper district of dams for hydropower plants. The forecasting of recession characteristics of the river flow after rainfalls is important with respect to system operation and dam management. We present a method for improving the precision of forecasting flow rate upper district of dams by utilizing steady-state estimation and recession time constant of the river flow. A case study was carried out on the upper district of the Yahagi River in Central Japan. It is found from our investigations that the forecasting accuracy is improved to 18.6% from 25.8% with a forecasted error of the total amount of river flow by using steady-state estimation.

A Multi-Dimensional Method for Nodal Load Forecasting

2014 ◽  
Vol 1070-1072 ◽  
pp. 708-717
Author(s):  
Zhi Yuan Pan ◽  
Chao Nan Liu ◽  
Jing Wang ◽  
Yong Wang
Keyword(s):  
Energy Flow ◽  
Load Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Load Pattern ◽  
And Control

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

scholarly journals Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Chuanjin Jiang ◽  
Jing Zhang ◽  
Fugen Song
Keyword(s):  
Model Selection ◽  
Empirical Analysis ◽  
Cointegration Test ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Encompassing Test ◽  
Accuracy And Stability ◽  
Selection Of

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.

scholarly journals Clustering-based forecasting method for individual consumers electricity load using time series representations

2018 ◽  
Vol 8 (1) ◽  
pp. 38-50 ◽  
Author(s):  
Peter Laurinec ◽  
Mária Lucká
Keyword(s):  
Time Series ◽  
Smart Grid ◽  
Series Representation ◽  
Smart Meter ◽  
Grid Data ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Series Representations ◽  
Electricity Load

Abstract This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately

scholarly journals PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (2) ◽  
pp. 194-207
Author(s):  
Riski Arum Pitaloka ◽  
Sugito Sugito ◽  
Rita Rahmawati
Keyword(s):  
Time Series ◽  
Trade Balance ◽  
Arima Model ◽  
Ensemble Averaging ◽  
High Increase ◽  
Forecasting Accuracy ◽  
Central Java ◽  
Forecasting Method ◽  
The Ensemble Method ◽  
The Government

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

scholarly journals Ex Ante Tourism Forecasting Assessment

2020 ◽  
pp. 004728752097445
Author(s):  
Anyu Liu ◽  
Vera Shanshan Lin ◽  
Gang Li ◽  
Haiyan Song
Keyword(s):  
Regression Analysis ◽  
International Tourism ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Factors Influencing ◽  
The Pacific ◽  
Forecasting Method ◽  
Ex Ante ◽  
Pacific Asia ◽  
Forecasting Errors

Although numerous studies have focused on forecasting international tourism demand, minimal light has been shed on the factors influencing the accuracy of real-world ex ante forecasting. This study evaluates the forecasting errors across various prediction horizons by analyzing the annually published forecasts of the Pacific Asia Tourism Association (PATA) from 2013 to 2017, comprising 765 origin–destination pairs covering 31 destinations in the region. The regression analysis shows that the variation in tourism demand and gross domestic product (GDP), covariation between tourism demand and GDP, order of lagged variables, origin, destination, and forecasting method all have significant effects on the forecasting accuracy over different horizons. This suggests that tourism forecasting should account for these factors in the future.

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