Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine Learning Approach

2021 ◽  
Vol 147 (4) ◽  
pp. 04021004
Author(s):  
Maria Xenochristou ◽  
Chris Hutton ◽  
Jan Hofman ◽  
Zoran Kapelan
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Learning Approach ◽  
Short Term ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach ◽  
Household Water ◽  
Short Term Forecasting ◽  
The Uk

scholarly journals Forecasting cryptocurrency prices time series using machine learning approach

2019 ◽  
Vol 65 ◽  
pp. 02001 ◽  
Author(s):  
Vasily Derbentsev ◽  
Natalia Datsenko ◽  
Olga Stepanenko ◽  
Vitaly Bezkorovainyi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Regression Trees ◽  
Learning Approach ◽  
Short Term ◽  
Modified Model ◽  
Machine Learning Approach ◽  
Auto Regressive ◽  
Standard Models ◽  
Short Term Forecasting

This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).

Using machine learning approach to predict short-term mortality risk of acute myocardial infarction after emergency admission

2021 ◽  
Author(s):  
Po-Cheng Peng ◽  
Hsu-Chun Chien ◽  
Prasenjit Mitra ◽  
Tun-Wen Pai ◽  
Chao-Hung Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Mortality Risk ◽  
Emergency Admission ◽  
Learning Approach ◽  
Short Term ◽  
Machine Learning Approach ◽  
Short Term Mortality

Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach

2020 ◽  
Vol 132 ◽  
pp. 104988 ◽  
Author(s):  
Cristian Arteaga ◽  
Alexander Paz ◽  
JeeWoong Park
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Injury Severity ◽  
Learning Approach ◽  
Traffic Crashes ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach

scholarly journals How Effective Was the UK Carbon Tax?—A Machine Learning Approach to Policy Evaluation

2019 ◽  
Author(s):  
Jan Abrell ◽  
Mirjam Kosch ◽  
Sebastian Rausch
Keyword(s):  
Machine Learning ◽  
Policy Evaluation ◽  
Carbon Tax ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
The Uk

scholarly journals A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences

2019 ◽  
Vol 6 (9) ◽  
pp. 190741 ◽  
Author(s):  
Domicele Jonauskaite ◽  
Jörg Wicker ◽  
Christine Mohr ◽  
Nele Dael ◽  
Jelena Havelka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Native Languages ◽  
Discrete Emotion ◽  
Promising Tool ◽  
Machine Learning Approach ◽  
The Uk ◽  
Decoding Accuracy ◽  
Colour Terms ◽  
Country Specific

The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour–emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour–emotion associations and (b) predicting the country of origin from the 240 individual colour–emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent colour-specific and that colour–emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the colour-decoding accuracy, was detectable but relatively small (6.1%), indicating that colour–emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analysing complex datasets from emotion research.

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