scholarly journals Adversarial Unsupervised Representation Learning for Activity Time-Series

2019 ◽  
Vol 33 ◽  
pp. 834-841 ◽  
Author(s):  
Karan Aggarwal ◽  
Shafiq Joty ◽  
Luis Fernandez-Luque ◽  
Jaideep Srivastava
Keyword(s):  
Time Series ◽  
Chronic Conditions ◽  
Empirical Evaluation ◽  
Representation Learning ◽  
Time Segment ◽  
Linear Classifiers ◽  
Daily Routines ◽  
Learning Technique ◽  
Prevention And Cure ◽  
Better Than

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.

scholarly journals Demand forecasting in Small and Medium Enterprises (SMEs) ED Aluminium Yogyakarta using causal, time series, and combined causal-time series approaches

2018 ◽  
Vol 204 ◽  
pp. 01004 ◽  
Author(s):  
Wildanul Isnaini ◽  
Andi Sudiarso
Keyword(s):  
Time Series ◽  
Small And Medium Enterprises ◽  
Demand Forecasting ◽  
Actual Condition ◽  
Inventory Data ◽  
Inventory Cost ◽  
Forecasting Method ◽  
Medium Enterprises ◽  
Better Than

ED Aluminium is the biggest Small and Medium Enterprises (SMEs) in Daerah Istimewa Yogyakarta (DIY) with 90 number of workers and 1,5 ton ingot capacity for production (Isnaini, 2014). Inventory data in December 2015 indicates that some products are overstocked (9%) and stockout (83%). This condition can happend because that SMEs still using intuition to predict the number of demand. Inventory fluctuation causes the inventory cost increases while overstock happend and lost the opportunity cost during stockout. To avoid overstock and stockout, the determination of demand with exact method is needed and one of them can be solved by forecasting method. This study aims to find the best forecasting methods of demand in 2015 using causal, time series, and combined causal-time series approces that better than the actual condition. The results of this research is the best forecasting method used to predict the number of sales in January-November 2015, that are SARIMA (3,1,1)(0,1,1)12 for WB, SARIMA (1,1,1)(1,0,1)6 for WSD, SARIMA (1,1,1)(1,1,0)6 for DE, SARIMA (2,1,1)(1,1,0)6 for PE, and SARIMA (2,1,3)(0,1,0)12 for PT.

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

scholarly journals Is Islamic Bank More Stable Than Conventional Bank? Evidence From Islamic Rural Banks in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 294
Author(s):  
Agus Widarjono ◽  
M. B. Hendrie Anto ◽  
Faaza Fakhrunnas
Keyword(s):  
Time Series ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Z Score ◽  
Profit Rate ◽  
Return On Assets ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Better Than

This study investigates whether Islamic rural banks perform better than conventional rural banks as their competitor in Indonesia. To measure Islamic rural banks' financial performance, we apply financial stability using Z-score and profitability using the return on assets. We use monthly time series data from January 2009 to December 2018. The dynamic regression of the Autoregressive Distributed Lag (ARDL) model is then employed. The results report that the Z-Score of Islamic rural banks is higher than the Z-Score of conventional rural banks. This finding shows that Islamic rural banks are less risky than conventional rural banks. However, the Islamic rural banks' financial stability is very vulnerable to changes in equity, output, and inflation than conventional rural banks. Although the Islamic rural banks' profit rate is lower compared to conventional rural banks, it is considered more stable. The profit of Islamic rural banks is affected by size, equity, domestic output, and inflation.

Corner Reflector Network as a Geodetic Reference for Landslide Monitoring via InSAR Time Series: Case Study from Slovakia

2021 ◽  
Author(s):  
Richard Czikhardt ◽  
Juraj Papco ◽  
Peter Ondrejka ◽  
Peter Ondrus ◽  
Pavel Liscak
Keyword(s):  
Time Series ◽  
Horizontal Displacement ◽  
Terrestrial Reference Frame ◽  
Sar Interferometry ◽  
Global Navigation Satellite Systems ◽  
Reference Network ◽  
Landslide Monitoring ◽  
First Order ◽  
Order Estimation ◽  
Better Than

<p>SAR interferometry (InSAR) is inherently a relative geodetic technique requiring one temporal and one spatial reference to obtain the datum-free estimates on millimetre-level displacements within the network of radar scatterers. To correct the systematic errors, such as the varying atmospheric delay, and solve the phase ambiguities, it relies on the first-order estimation network of coherent point scatterers (PS).</p><p>For vegetated and sparsely urbanized areas, commonly affected by landslides in Slovakia, it is often difficult to construct a reliable first-order estimation network, as they lack the PS. Purposedly deploying corner reflectors (CR) at such areas strengthens the estimation network and, if these CR are collocated with a Global Navigation Satellite Systems (GNSS), they provide an absolute geodetic reference to a well-defined terrestrial reference frame (TRF), as well as independent quality control.</p><p>For landslides, line-of-sight (LOS) InSAR displacements can be difficult to interpret. Using double CR, i.e. two reflectors for ascending/descending geometries within a single instrument, enables the assumption-less decomposition of the observed cross-track LOS displacements into the vertical and the horizontal displacement components.</p><p>In this study, we perform InSAR analysis on the one-year of Sentinel-1 time series of five areas in Slovakia, affected by landslides. 24 double back-flipped trihedral CR were carefully deployed at these sites to form a reference network, guaranteeing reliable displacement information over the critical landslide zones. To confirm the measurement quality, we show that the temporal average Signal-to-Clutter Ratio (SCR) of the CR is better than 20 dB. The observed CR motions in vertical and east-west directions vary from several millimetres up to 3 centimetres, with average standard deviation better than 0.5 mm.<br>Repeated GNSS measurements of the CR confirm the displacement observed by the InSAR, improve the positioning precision of the nearby PS, and attain the transformation into the national TRF.</p>

Convolutional Autoencoder for Unsupervised Representation Learning of PolSAR Time-Series

2021 ◽  
Author(s):  
Thomas Di Martino ◽  
Regis Guinvarc'h ◽  
Laetitia Thirion-Lefevre ◽  
Elise Colin Koeniguer
Keyword(s):  
Time Series ◽  
Representation Learning ◽  
Convolutional Autoencoder
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