A study on leading machine learning techniques for high order fuzzy time series forecasting

2020 ◽  
Vol 87 ◽  
pp. 103245 ◽  
Author(s):  
Sibarama Panigrahi ◽  
H.S. Behera
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

2017 ◽  
Vol 226 ◽  
pp. 012117 ◽  
Author(s):  
Kasturi Kanchymalay ◽  
N. Salim ◽  
Anupong Sukprasert ◽  
Ramesh Krishnan ◽  
Ummi Raba’ah Hashim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Palm Oil ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Oil Price ◽  
Machine Learning Techniques ◽  
Crude Palm Oil ◽  
Learning Techniques

Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting

2021 ◽  
Vol 12 (2) ◽  
pp. 36-51
Author(s):  
Wasiur Rhmann
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Software Maintenance ◽  
Time Series Forecasting ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Software Change ◽  
Learning Techniques ◽  
Change Prediction ◽  
Good For

Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.

A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning

2021 ◽  
Author(s):  
Prasanna Amur Varadarajan ◽  
Ghislain Roguin ◽  
Nick Abolins ◽  
Maurice Ringer
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Event Detection ◽  
Hybrid Approach ◽  
Time Series Forecasting ◽  
Machine Learning Techniques ◽  
Construction Operations ◽  
Model Comparisons ◽  
Learning Techniques

Abstract Abnormal hydraulic event detection is essential for offshore well construction operations. These operations require model comparisons and real-time measurements. For this task, physics-based models, which need frequent manual calibration do not accurately capture all the hydraulic trends. The paper presents a method to overcome existing limitations by combining physics-based models with machine learning techniques, which are suited for time series forecasting. This method ensures accurate and reliable predictions during the forecasting period and helps remove the need for frequent manual calibration of the hydraulic input parameters.

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

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