Ling et al 2020 Journal of Forecasting

Price information is a crucial market information for a farmer. The price instability and uncertainty pose a significant challenge to decision makers in making proper production and marketing plans to minimize risk. Agricultural price series cannot be modelled and predicted accurately by traditional econometric models owing to its nonlinearity and nonstationary behaviour. In the present study an attempt has been made to model and predict price series using Empirical Mode Decomposition (EMD) based Support Vector Regression (SVR) model. EMD decomposes the original nonlinear and nonstationary dataset into a finite and small number of sub-signals. Then each sub-signal was modelled and forecasted by SVR method. Finally, all the forecasted values of sub-signal were aggregated to make final ensemble forecast. The effectiveness and predictability of the proposed methodology was verified using Chilli wholesale price index (WPI) dataset as sample. The results indicated that the performance of the proposed model was substantially superior as compared to the standard SVR.

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 2021

The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.

Malaysian Journal of Fundamental and Applied Sciences

This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN.

Computer Modeling in Engineering & Sciences, 2022

This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. Wavelet Decomposition (WD) algorithm has been combined with various traditional forecasting time-series models, such as Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS) and their effects are examined in terms of the statistical estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance basis. Therefore, combining wavelet forecasting models has yielded much better results.

Bhartiya Krishi Anusandhan Patrika

Agricultural price information needs for decision-making at all levels are increasing due to globalization and market integration. Due to its great reliance on biological processes, agricultural price forecasting is one of the most difficult fields of time series analysis. In this paper, a neural network model based on empirical mode decomposition is used to forecast potato prices. The monthly wholesale price series of potato from Chennai market was decomposed into five independent intrinsic modes (IMFs) and one residual with various frequencies. Then, to forecast these IMFs and residual components independently, an artificial neural network with a single hidden layer was built. Finally, the ensemble output for the original price series is formed by aggregating the forecast outcomes of all IMFs, including residuals. In terms of root mean square error and directional prediction statistics, empirical data show that the suggested ensemble model outperforms a single model.

2018 International Conference on Information and Computer Technologies (ICICT), 2018

There is a necessity to anticipate and identify changes in events points to a new direction in the stock exchange markets in line with the analysis of the oscillations of prices of financial assets. This need leads to argue about new alternatives in the prediction of financial time series using machine learning methods. This paper aims to describe the development of the SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of financial time series, particularly applied to Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The adjusted series are compared with traditional models such as ARIMA and ARFIMA Model. In Addition, statistical tests like normality and unit root are performed to prove that the series in question have non-linear distribution and also to verify the level of correlation between the periods of the series.

Malaysian Journal of Fundamental and Applied Sciences

Forecasting of Crude Palm Oil (CPO) is one of the most important and the largest vegetable oil traded in the world market. This study investigates the forecasting of Crude Palm Oil (CPO) price using a hybrid model of Group Method of Data Handling (GMDH) with wavelet decomposition. The original monthly data of CPO time series were decomposed into the spectral band. After that, these decomposed subseries were given as input time series data to GMDH model to forecast the CPO price of monthly time series data. The result performance of hybridized GMDH model is compared with the original GMDH model. The measurements results from the mean absolute error (MAE) and the root mean square error (RMSE) showed that the hybrid GMDH model with wavelet decomposition gives more accurate result of predictions compared with the original GMDH model.

International Journal on Advanced Science, Engineering and Information Technology, 2017

In this paper, a hybrid time series forecasting approach is proposed consisting of wavelet transform as the data decomposition method with Autoregressive Integrated Moving Average (ARIMA) and Least Square Support Vector Machine (LSSVM) combination as the forecasting method to enhance the accuracy in forecasting the crude oil spot prices (COSP) series. In brief, the original COSP is divided into a more stable constitutive series using discrete wavelet transform (DWT). These respective sub-series are then forecasted using ARIMA and LSSVM combination method, and lastly, all forecasted components are combined back together to acquire the original forecasted series. The datasets consist of monthly COSP series from West Texas Intermediate (WTI) and Brent North Sea (Brent). To evaluate the effectiveness of the proposed approach, several comparisons are made with the single forecasting approaches, a hybrid forecasting approach and also some existing forecasting approaches that utilize COSP series as the dataset by comparing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) acquired. From the results, the proposed approach has managed to outperform the other approaches with smaller MAE and RMSE values which signify better forecasting accuracy. Ultimately, the study proves that the integration of data decomposition with forecasting combination method could increase the accuracy of COSP series forecasting.

The Scientific World Journal, 2014

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms th...

ILKOM Jurnal Ilmiah

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in th...