scholarly journals Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shiwei Su
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Linear And Nonlinear ◽  
True Values

In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

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.

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 Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 1 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Modelling monthly influenza cases in Malaysia

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254137
Author(s):  
Muhammad Adam Norrulashikin ◽  
Fadhilah Yusof ◽  
Nur Hanani Mohd Hanafiah ◽  
Siti Mariam Norrulashikin
Keyword(s):  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Healthcare Sector ◽  
Support Vector ◽  
Test Dataset ◽  
Autoregressive Integrated Moving Average ◽  
Health Organization ◽  
Nonlinear Components

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

Gold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

2020 ◽  
Vol 59 (7) ◽  
pp. 1239-1259
Author(s):  
Dehe Xu ◽  
Qi Zhang ◽  
Yan Ding ◽  
Huiping Huang
Keyword(s):  
Time Scale ◽  
Interpolation Method ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Kriging Interpolation ◽  
Support Vector ◽  
Kriging Interpolation Method

AbstractDrought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Statistical Analysis and Prediction of COVID-19 outbreak in India using Machine Learning

2020 ◽  
pp. 199-207
Author(s):  
Akshar Patel ◽  
Dweepna Garg
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Support Vector ◽  
Health Security ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Svm Model ◽  
The Government ◽  
Communication Paths ◽  
Model Support

Coronavirus disease globally known as COVID-19 is triggered by SARS-COV2. It is the predominant cause of an extremely dangerous disease that has bothered global health security. It is proposed that COVID-19 might be zoonotically based on the high number of people exposed in Wuhan City, China, to the wet animal market[1]. COVID-19 is a severe acute respiratory disease, transmitted by respiratory secretions and communication paths, as of WHO reports. The disease is spreading throughout the world at a faster pace. The first instance of COVID-19 was firstly discovered and found in Wuhan, Hubei Province, China in December 2019[1]. This paper analyses the outbreak of this disease until June 22, 2020, for India and other top major affected nations and also predictions were made regarding the number of cases for India over the next 17 days i.e from 23 June 2020 to 9 July 2020. Linear Regression model, Support Vector Machine Regressor (SVM) model, Autoregressive Integrated Moving Average (ARIMA) model and Facebook's Prophet model were used for prediction based on the Kaggle downloaded dataset with data collected from January 22, 2020, to June 22, 2020. By 22 June 2020, the disease has spread across more than 200 countries, reporting 12,322 confirmed cases, 45,26,333 recovered cases and 4,72,171 COVID-19 deaths. Assessment of this epidemic allows the Government to take the appropriate steps to curb the threat of this global pandemic.

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