scholarly journals Forecasting of crude palm oil price using hybridizing wavelet and group method of data handling model

2017 ◽  
Vol 13 (4) ◽  
pp. 642-648
Author(s):  
Huma Basheer ◽  
Azme Khamis
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Wavelet Decomposition ◽  
Time Series Data ◽  
Spectral Band ◽  
Absolute Error ◽  
Series Data ◽  
Group Method ◽  
Data Handling ◽  
Crude Palm Oil

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.

A review of irregular time series data handling with gated recurrent neural networks

2021 ◽  
Vol 441 ◽  
pp. 161-178
Author(s):  
Philip B. Weerakody ◽  
Kok Wai Wong ◽  
Guanjin Wang ◽  
Wendell Ela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Handling

scholarly journals Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Time Series Data ◽  
Series Data ◽  
Correction Algorithm ◽  
Data Sequence ◽  
Variation Pattern ◽  
Offset Correction ◽  
Changing Trend ◽  
Track Irregularity

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.

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.

Time series prediction using machine learning: a case of Bitcoin returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irfan Haider Shakri
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Predictive Ability ◽  
Absolute Error ◽  
Data Driven ◽  
Series Data ◽  
Policy Uncertainty ◽  
Content Type

Purpose The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms. Design/methodology/approach The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking. Findings Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns. Originality/value This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

scholarly journals TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment

2021 ◽  
Vol 10 (4) ◽  
pp. 267
Author(s):  
Inder Tecuapetla-Gómez ◽  
Gerardo López-Saldaña ◽  
María Isabel Cruz-López ◽  
Rainer Ressl
Keyword(s):  
Time Series ◽  
Open Source ◽  
Satellite Data ◽  
Time Series Data ◽  
Earth Observation ◽  
Quality Analysis ◽  
Quality Data ◽  
Series Data ◽  
Data Handling ◽  
Geographical Regions

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

scholarly journals Times Series Averaging and Denoising from a Probabilistic Perspective on Time–Elastic Kernels

2019 ◽  
Vol 29 (2) ◽  
pp. 375-392 ◽  
Author(s):  
Pierre-Francois Marteau
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Spectral Band ◽  
Heuristic Method ◽  
Synthetic Data ◽  
Series Data ◽  
Data Sets ◽  
Robust Model ◽  
The Times ◽  
Dynamic Time

Abstract In the light of regularized dynamic time warping kernels, this paper re-considers the concept of a time elastic centroid for a set of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of their occurrence. By comparing classification accuracies for 45 heterogeneous time series data sets obtained by first nearest centroid/medoid classifiers, we show that (i) centroid-based approaches significantly outperform medoid-based ones, (ii) for the data sets considered, our algorithm, which combines averaging in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to significantly reduce the size of training instance sets. Finally, we highlight its denoising capability using demonstrative synthetic data. Specifically, we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.

scholarly journals Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising

2017 ◽  
Vol 49 (3) ◽  
pp. 711-723 ◽  
Author(s):  
Xiaorong Lu ◽  
Xuelei Wang ◽  
Liang Zhang ◽  
Ting Zhang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Wavelet Decomposition ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Gene Expression Programming ◽  
Stationary Time Series ◽  
Series Data ◽  
Effective Measure

Abstract Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow time series data are initially decomposed into one sub-signal approximation and seven sub-signal details using the dmey wavelet. A wavelet threshold de-noising method is also applied in this study. Data that have been de-noised after decomposition are then adopted as inputs for WDDGEP models. Finally, the forecasted sub-signal results are summed to formulate an ensemble forecast for the original monthly flow series. A comparison of the prediction accuracy between the two models is based on three performance evaluation measures. Results show that the new WDDGEP models can effectively enhance accuracy in forecasting streamflow, and the proposed wavelet-based de-noising of the observed non-stationary time series is an effective measure to improve simulation accuracy.

scholarly journals Perspective on Crude Palm Oil Production: The Effect of Raw Palm Oil and Biofuel Prices

2021 ◽  
pp. 15-25
Author(s):  
Afriany ◽  
Rubianto Pitoyo
Keyword(s):  
Production Process ◽  
Palm Oil ◽  
Time Series Data ◽  
Oil Production ◽  
Series Data ◽  
Production Data ◽  
Crude Palm Oil ◽  
Specialized Production ◽  
Production Factors ◽  
The Relationship

Efficiency is the important things in production process. Some production factors as labor, materials, and machinery must be calculate accurately. The purpose of this research is for analyzing the influence of raw palm oil and Biofuel prices against crude palm oil production. Sample in this research is time series data that specialized production data, The technique analysis is using analysis of multiple linier regression. The results from analysis show correlation between raw palm oil and biofuel prices with crude palm oil production is 57.1 %, The relationship between raw palm oil against crude palm oil production have significant effect and biofuel prices against crude palm oil production have no significant effect. Based on the results of F test there are the significant influence between raw palm oil and biofues prices against crude palm oil production. finding in this research is PT. Wilmar using 3 type of fuel for production process, petroleum, biofuel and waste of raw palm oil production which makes biofuel prices have no effect on crude palm oil production

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