scholarly journals A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA

2021 ◽  
Author(s):  
Emilly Pereira Alves ◽  
Joao Fausto Lorenzato Oliveira ◽  
Manoel Henrique da Nóbrega Marinho ◽  
Francisco Madeiro
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Linear Pattern ◽  
Squared Error ◽  
The Subject

In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combination model based on SVR and ARIMA is proposed. The main difference in comparison with other works is the use of an interactive Particle Swarm Optimization (PSO) to increase the prediction performance. To the experimental setup, six well-known datasets of the literature is used. The performance is assessed by the metrics Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show the proposed system attains better outcomes when compared to the other tested techniques, for most of the used data.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

MATRIX FORMULAS FOR NONSTATIONARY ARIMA SIGNAL EXTRACTION

2008 ◽  
Vol 24 (4) ◽  
pp. 988-1009 ◽  
Author(s):  
Tucker McElroy
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Moving Average ◽  
Signal Extraction ◽  
Error Signal ◽  
Autoregressive Integrated Moving Average ◽  
General Matrix ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Moving Average Processes

The paper provides general matrix formulas for minimum mean squared error signal extraction for a finitely sampled time series whose signal and noise components are nonstationary autoregressive integrated moving average processes. These formulas are quite practical; in addition to being simple to implement on a computer, they make it possible to easily derive important general properties of the signal extraction filters. We also extend these formulas to estimates of future values of the unobserved signal, and we show how this result combines signal extraction and forecasting.

scholarly journals Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
pp. 101-110
Author(s):  
Evizal Abdul Kadir ◽  
Nur Ezzati Dayana ◽  
Sri Listia Rosa ◽  
Mahmod Othman ◽  
Rizauddin Saian
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Mean Squared Error ◽  
Moving Average ◽  
Arima Model ◽  
Forest Damage ◽  
Autoregressive Integrated Moving Average ◽  
Squared Error ◽  
Proposed Model ◽  
Forecasting System

Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

scholarly journals Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
Jaka Darma Jaya
Keyword(s):  
Trend Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Polynomial Trend ◽  
Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri.  Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional.  Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis.  Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error).  Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12;  MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

scholarly journals Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hye-Jin Kim ◽  
Sung Min Park ◽  
Byung Jin Choi ◽  
Seung-Hyun Moon ◽  
Yong-Hyuk Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Quality Control ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Weather Element ◽  
Applied Machine Learning ◽  
Squared Error ◽  
Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

scholarly journals Wavelet Transform and Autoregressive Integrated Moving Average Combination Approach in Crude Oil Prices Forecasting

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Nurull Qurraisha Nadiyya Md-Khair ◽  
Ruhaidah Samsudin ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Moving Average ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Autoregressive Integrated Moving Average ◽  
Spot Prices ◽  
Crude Oil Prices ◽  
Combination Approach

This paper proposes a time series forecasting approach combining wavelet transform and autoregressive integrated moving average (ARIMA) to enhance the precision in forecasting crude oil spot prices series. Wavelet transform splits the original prices series into several subseries, then the most appropriate model of ARIMA is established to predict each respective series and finally all series are combined back to get the original series. The datasets for the experiment consist of crude oil spot prices from Brent North Sea (Brent) and West Texas Intermediate (WTI). Single forecasting model ARIMA and several existing forecasting approaches in the literatures are used to measure the performance of the proposed approach by utilizing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) collected. Final results have depicted that the proposed approach outperforms other approaches with smaller MAE and RMSE values. Ultimately, it is proven that data decomposition, combined with forecasting method can increase the accuracy in time series forecasting.

scholarly journals Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 16-32
Author(s):  
Ahmed Hassan Mohammed Hassan ◽  
◽  
Arfan Ali Mohammed Qasem ◽  
Walaa Faisal Mohammed Abdalla ◽  
Omer H. Elhassan
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Future Perspective ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines ◽  
The World ◽  
Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

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.

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