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# System prepended metadata

title: Irregular Time Series Breakout Notes

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# Irregular Time Series Breakout Notes

## K. Zhang (UC Berkeley) -  “Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks”

 - State of the Art (SOTA) results in variable star classification with periodic networks
 - Andronov: fast photometers -> smaller bins
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## Monika Soraisam (UUIC), "Challenges and Opportunities in Real-Time Astronomy"

 - Rubin/LSST: barage of data (15 TB/night)
 - Outlier detection algorithm, applied to ZTF/DECCam
 - How are algorithms going to adapt (C. Alves)?
 - How long does it take to detect 

## Jakob Robnik (UC Berkeley), “Probabilistic Exoplanet Search with Fourier Methods”

 - Fourier GP to fill out seasonal gaps
 - matched filter: accurate & fast 
 - "planet signature is correlated by outliers are not"
 - false positive rate from Bayes factor (Bayer & Seljak)

## Ashley Villar (Columbia), “Learning with Irregular Time Series of Transients and Variables”

 - Supernova light curves for physics: aperiodic, irregularly-spaced, and multi-band sparse light curves
 - Supernovae/transients <-> medical connection
 - Recurrent variational autoencoder (interpolating LCs with 2-dim GP), real-time update.
 - some interpolation leads to better results (even if poor)
 - Static latent space despite the evolving time-series
 - long timescale: Park+21 2106.01450 (generative processes)
 - masking instability

## Open Q&A
Bayesian Lomb-Scargle (2015) from Evan Smith


## Eric Bellm
- Variability of astrophysical sources
- Timeseries features for alert production (Richards et al., 2011); periodic and aperiodic
- More feauters are needed; community inputs/feedback will be crucial (community.lsst.org)
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## Pablo Huijse
- ALeRCE broker: Multiband periodicity
- Automatic search in light curves
- multicolor period is the most important feature in classification, but it's the most expensive feature to generate
- Sinewaves vs folding
- Compute Mutural information from joint phase-mag space.
- Detection via significance of a mode in periodogram and false alarm prob.
- ideas: use a NN to determine signifance of periodogram

## R. Martínez-Galarza

- anomaly detection (TESS/Gaia)
- Anomaly, recovery rate, analogs to anomaly?
- Random forest on light curves and periodograms (Kepler); anomaly score
- Important feasture for classification useful for anomaly detection?
- Kepler case: about 20% unclassified
- 30 min to 3 hours are needed for more than 80% recovery rate
- Question about experiemental design (to avoid window aliasing eg.)
- RF-based anomaly detection https://iopscience.iop.org/article/10.1088/0067-0049/203/2/32#apjs447279s3-5


## Elizabeth Melton
- ARIMA: remove autocorrelated nose from time series
- Autoregressive behavior in Kepler light curve
- ARMA for regularly-spaced stationary and constant variance.
- ARIMA(p, d, q) remove non-stationarity
- For irregular light curves? 1. 90% missing? Not much impact if missing and observed are gropued together 2. dense cadence needed
- ARIMAX (exogeneous), ARFIMA (power law trends)
- CARMA/CARFIMA (irregular sampled continueous time series)
- SARIMA (periodic)

## Panos Toulis
- Peak in a periodogram. Is the peak near the true peak? Not always near the truth.
- The asymptotic is broken if likelihood is not Gaussian-like, non-iid observation times.
- A randomized test can provide a sampling distribution of the peak. No Gaussian assumption / robust inference
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