TY  - JOUR
A1  - Zhelayskaya, Irina S.
A1  - Vasile, Ruggero
A1  - Shprits, Yuri Y.
A1  - Stolle, Claudia
A1  - Matzka, Jürgen
T1  - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index
JF  - Space Weather: The International Journal of Research and Applications
N2  - The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
KW  - Kp index
KW  - Predictive models
KW  - Feature selection
KW  - Machine learning
KW  - Validation
Y1  - 2019
U6  - https://doi.org/10.1029/2019SW002271
SN  - 1542-7390
VL  - 17
IS  - 10
SP  - 1461
EP  - 1486
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - GEN
A1  - Kayhan Wagner, Ezgi
A1  - Meyer, Marlene
A1  - O’Reilly, J.X.
A1  - Hunnius, Sabine
A1  - Bekkering, Harold
T1  - Nine-month-old infants update their predictive models of a changing environment
T2  - Postprints der Universität Potsdam Humanwissenschaftliche Reihe
N2  - Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.
T3  - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 577 
KW  - Internal models
KW  - Predictive models
KW  - Predictive processing
KW  - Development
KW  - Event-Related potentials
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-437844
SN  - 1866-8364
IS  - 577
ER  - 
TY  - JOUR
A1  - Kayhan Wagner, Ezgi
A1  - Meyer, Marlene
A1  - O’Reilly, J.X.
A1  - Hunnius, Sabine
A1  - Bekkering, Harold
T1  - Nine-month-old infants update their predictive models of a changing environment
JF  - Developmental Cognitive Neuroscience : a journal for cognitive, affective and social developmental neuroscience
N2  - Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.
KW  - Internal models
KW  - Predictive models
KW  - Predictive processing
KW  - Development
KW  - Event-Related potentials
Y1  - 2019
U6  - https://doi.org/10.1016/j.dcn.2019.100680
SN  - 1878-9293
SN  - 1878-9307
VL  - 38
PB  - Elsevier
CY  - Amsterdam
ER  -