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.

scholarly journals Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Luca Pipia ◽  
Eatidal Amin ◽  
Santiago Belda ◽  
Matías Salinero-Delgado ◽  
Jochem Verrelst
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Gap Filling ◽  
Area Index ◽  
Uncertainty Estimates ◽  
Google Earth Engine

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.

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
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