The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach

2019 ◽  
Author(s):  
Haifeng Wang ◽  
Harshdeep Ahluwalia ◽  
Roger A Aliaga-Diaz ◽  
Joseph H. Davis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Equity Market ◽  
Market Returns ◽  
Learning Time ◽  
Time Series Approach ◽  
Hybrid Machine

The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 9-20
Author(s):  
Haifeng Wang ◽  
Harshdeep Singh Ahluwalia ◽  
Roger A. Aliaga-Díaz ◽  
Joseph H. Davis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Equity Market ◽  
Market Returns ◽  
Learning Time ◽  
Time Series Approach ◽  
Hybrid Machine

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

2020 ◽  
Vol 127 ◽  
pp. 104666 ◽  
Author(s):  
Santiago Belda ◽  
Luca Pipia ◽  
Pablo Morcillo-Pallarés ◽  
Juan Pablo Rivera-Caicedo ◽  
Eatidal Amin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Vegetation Phenology ◽  
Gap Filling ◽  
Learning Time

scholarly journals COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

2020 ◽  
Vol 10 (11) ◽  
pp. 3880 ◽  
Author(s):  
Vasilis Papastefanopoulos ◽  
Pantelis Linardatos ◽  
Sotiris Kotsiantis
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Time Series ◽  
Health System ◽  
Total Population ◽  
Outbreak Detection ◽  
Time Series Modeling ◽  
Learning Time ◽  
Raising Awareness ◽  
The Impact

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

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