Chaos Time Series Prediction Based on Membrane Optimization Algorithms

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction(τ,m)and least squares support vector machine (LS-SVM)(γ,σ)by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Remote Sensing ◽

10.3390/rs12111814 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1814

Author(s):

Phamchimai Phan ◽

Nengcheng Chen ◽

Lei Xu ◽

Zeqiang Chen

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Vegetation Index ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

Atmosphere ◽

10.3390/atmos12101341 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1341

Author(s):

Yuju Ma ◽

Liyuan Zuo ◽

Jiangbo Gao ◽

Qiang Liu ◽

Lulu Liu

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Human Activities ◽

Vegetation Index ◽

Terrestrial Ecosystem ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Response Factors

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

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Peramalan Data Ekspor Nonmigas Provinsi Kalimantan Timur Menggunakan Metode Weighted Fuzzy Time Series Lee

J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika ◽

10.36456/jstat.vol14.no1.a3747 ◽

2021 ◽

Vol 14 (1) ◽

pp. 1-10

Author(s):

Muhammad Wahdeni Pramana ◽

Ika Purnamasari ◽

Surya Prangga

Keyword(s):

Time Series ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Percentage Error ◽

Mean Square ◽

Absolute Percentage Error

Ekspor merupakan aktivitas perdagangan atau penjualan barang dari dalam negeri ke luar negeri. Ekspor nonmigas sebagai salah satu komponen pembentuk Produk Domestik Regional Bruto (PDRB) sehingga perlu adanya suatu peramalan nilai di masa mendatang. Fuzzy Time Series (FTS) merupakan metode peramalan dengan berdasarkan teori himpunan fuzzy, logika fuzzy, serta hasil peramalan yang dapat dibahasakan (linguistik). Metode Weighted Fuzzy Time Series (WFTS) Lee merupakan perluasan dari metode FTS dengan penambahan pembobotan pada tiap pola relasi yang terbentuk. Tujuan penelitian ini adalah memperoleh nilai peramalan ekspor nonmigas Provinsi Kalimantan Timur pada bulan November 2020 serta memperoleh nilai akurasi peramalan berdasarkan metode Mean Absolute Percentage Error (MAPE) dan Root Mean Square Error (RMSE). Berdasarkan hasil analisis diperoleh nilai akurasi peramalan untuk data Ekspor Nonmigas Provinsi Kalimantan Timur bulan Januari 2019 – Oktober 2020 dengan konstanta pembobot menggunakan metode MAPE diperoleh hasil keseluruhan dibawah 10% sehingga diperoleh konstanta pembobot terbaik yaitu dengan nilai MAPE terminimum yaitu sebesar 3,62% dan RMSE minimum sebesar 50,67. Dari hasil tersebut, diperoleh hasil peramalan untuk bulan November 2020 dengan menggunakan kontanta pembobot terbaik yaitu sebesar 850,96 juta USD.

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A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks

Recent Advances in Computer Science and Communications ◽

10.2174/1573401315666190619112842 ◽

2020 ◽

Vol 13 (5) ◽

pp. 827-832

Author(s):

Iflah Aijaz ◽

Parul Agarwal

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hybrid Models ◽

Time Series Forecasting ◽

Percentage Error ◽

Mean Square

Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

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COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

Journal of Mechanical Engineering ◽

10.3329/jme.v47i1.35354 ◽

2018 ◽

Vol 47 (1) ◽

pp. 16-21 ◽

Cited By ~ 1

Author(s):

Syed Misbah Uddin ◽

Aminur Rahman ◽

Emtiaz Uddin Ansari

Keyword(s):

Time Series ◽

Mean Square Error ◽

Moving Average ◽

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Mean Square ◽

Absolute Deviation ◽

Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

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PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

Jurnal Informatika Polinema ◽

10.33795/jip.v6i3.314 ◽

2020 ◽

Vol 6 (3) ◽

pp. 29-36

Author(s):

Deddy Kusbianto ◽

Agung Pramudhita ◽

Nurhalimah

Keyword(s):

Time Series ◽

Mean Square Error ◽

Moving Average ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Mean Square ◽

Absolute Deviation ◽

Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

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Multiple-Forecasting Approach: A Prediction of CO2 Emission from the Paddy Crop in India

10.21203/rs.3.rs-767860/v1 ◽

2021 ◽

Author(s):

Pawan Kumar Singh ◽

Alok Kumar Pandey ◽

Sahil Ahuja ◽

Ravi Kiran

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Co2 Emission ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Absolute Percentage Error ◽

The Mean ◽

Paddy Crop

Abstract This paper compares four prediction methods namely Random Forest Regressor (RFR), SARIMAX, Holt-Winters (H-W), and the Support Vector Regression (SVR) to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models to suggest an effective model to predict the total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crop in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE), highlights the differences in accuracy among the four models. The mean absolute percentage error (MAPE) and the mean square error (MSE) for the four methods are: RFR (MAPE: 5.67; MSE: 549900.02), SARIMAX (MAPE:1.67; MSE:70422.35), H-W (MAPE:0.75; MSE:16648.58), and SVR (MAPE: 0.91; MSE: 17832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the Support Vector Regression (SVR) is relatively low as compared to SARIMAX and RFR. On the basis of these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crop. Holt-Winters the model predicted 14364.97 for the year 2025 and SVR predicted 13696.67 for the year 2025. These predictions can be used by the decision-maker to build a suitable policy for future studies. For further research, this approach can be contrasted with other approaches, such as the Neural Network or other forecasting methods, using more important datasets to train the model to achieve better forecast accuracy.

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DEVELOPMENT OF RAINFALL FORECASTING MODEL USING MACHINE LEARNING WITH SINGULAR SPECTRUM ANALYSIS

IIUM Engineering Journal ◽

10.31436/iiumej.v23i1.1822 ◽

2022 ◽

Vol 23 (1) ◽

pp. 172-186

Author(s):

Pundru Chandra Shaker Reddy ◽

Sucharitha Yadala ◽

Surya Narayana Goddumarri

Keyword(s):

Random Forest ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Support Vector ◽

Mean Square ◽

Singular Spectrum ◽

Proposed Model

Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall. ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif.

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Estimating the wear rate of diamond cutting wire segment in building stone cutting using SVR and GA_MLP system

10.21203/rs.3.rs-435024/v1 ◽

2021 ◽

Author(s):

Omid Jami Babajan ◽

Raheb Bagherpour

Keyword(s):

Wear Rate ◽

Mean Square Error ◽

Building Stone ◽

Rock Properties ◽

Polynomial Kernel ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Diamond Cutting ◽

Wire Segment

Abstract As the cutting stone is a wear process, performing this process with diamond pieces' aid can be considered the wear of stone particles bypassing diamond grains on its surface. To better understand this process as well as the conditions governing the cutting diamond grain, it is necessary to familiar with the cutting mechanism along with the affecting parameters. In this matter, predicting the amount of segment consumption plays a prominent role to estimate the production cost as well as to schematize the building stone mines. This paper utilized the data obtained from Carbonate and Granite stones to estimate the amount of consumption of diamond cutting wire segments. To do so, two methods, namely support vector regression (SVR) and genetic algorithm + Multilayer perceptron (GA-MLP) were chosen using the MATLAB software toolboxes in order to estimate the segment erosion. In each of the above algorithms, a lowpass smoothing filter, called Savitzky-Golay was employed on the data. For this purpose, three rock properties including uniaxial compressive strength, Shimazk friction factor, and Young's modulus, were also employed as the model's input. After that, twelve models were constructed and then the segment erosion was estimated as well. Ultimately, the accuracy of the above models was assessed using the coefficient of determination (\({R}^{2}\)), mean square error (MSE), root mean square error (RMSE), mean value absolute error (MAD), mean absolute percentage error (MAPE), and variance of factor analysis (VFA). According to the obtained results, it can be concluded that the SVR approach and the Savitzky-Golay filter with Polynomial Kernel could better estimate the wear rate of the diamond cutting wire segment.

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