Use of A Data-Driven Approach for Time Series Prediction in Fault Prognosis of Satellite Reaction Wheel

2020 ◽  
Author(s):  
Md Sirajul Islam ◽  
Afshin Rahimi
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Data Driven ◽  
Reaction Wheel ◽  
Data Driven Approach ◽  
Fault Prognosis

Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques

2020 ◽  
Vol 146 (7) ◽  
pp. 04020013 ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Parveen Sihag ◽  
Ani Bin Shabri ◽  
Balraj Singh ◽  
Quoc Bao Pham
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Data Driven ◽  
Using Data

scholarly journals Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach

2018 ◽  
Vol 28 (7) ◽  
pp. 075502 ◽  
Author(s):  
Francisco Traversaro ◽  
Francisco O. Redelico ◽  
Marcelo R. Risk ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach

Data-Driven Approach for Fault Prognosis of SiC MOSFETs

2020 ◽  
Vol 35 (4) ◽  
pp. 4048-4062 ◽  
Author(s):  
Weiqiang Chen ◽  
Lingyi Zhang ◽  
Krishna Pattipati ◽  
Ali M. Bazzi ◽  
Shailesh Joshi ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Fault Prognosis

A data-driven approach for denoising GNSS position time series

2017 ◽  
Vol 92 (8) ◽  
pp. 905-922 ◽  
Author(s):  
Yanyan Li ◽  
Caijun Xu ◽  
Lei Yi ◽  
Rongxin Fang
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach ◽  
Position Time Series ◽  
Gnss Position Time Series

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.

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