Closure to “Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques” by Siraj Muhammed Pandhiani, Parveen Sihag, Ani Bin Shabri, Balraj Singh, and Quoc Bao Pham

2021 ◽  
Vol 147 (9) ◽  
pp. 07021015
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

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

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 Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods"

2021 ◽  
Author(s):  
Ather Abbas ◽  
Laurie Boithias ◽  
Yakov Pachepsky ◽  
Kyunghyun Kim ◽  
Jong Ahn Chun ◽  
...  
Keyword(s):  
Time Series ◽  
Open Source ◽  
Data Driven ◽  
Supplementary Material ◽  
Using Data ◽  
Python Package

Chaotic wind power time series prediction via switching data-driven modes

2020 ◽  
Vol 145 ◽  
pp. 270-281 ◽  
Author(s):  
Tinghui Ouyang ◽  
Heming Huang ◽  
Yusen He ◽  
Zhenhao Tang
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Time Series Prediction ◽  
Data Driven

Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network

2021 ◽  
Vol 29 (1) ◽  
pp. 4-18
Author(s):  
Jiabao Wen ◽  
Jiachen Yang ◽  
Bin Jiang ◽  
Houbing Song ◽  
Huihui Wang
Keyword(s):  
Time Series ◽  
Big Data ◽  
Marine Environment ◽  
Time Series Prediction ◽  
Data Driven
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