36 research outputs found
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization
We investigate nonlinear prediction in an online setting and introduce a
hybrid model that effectively mitigates, via an end-to-end architecture, the
need for hand-designed features and manual model selection issues of
conventional nonlinear prediction/regression methods. In particular, we use
recursive structures to extract features from sequential signals, while
preserving the state information, i.e., the history, and boosted decision trees
to produce the final output. The connection is in an end-to-end fashion and we
jointly optimize the whole architecture using stochastic gradient descent, for
which we also provide the backward pass update equations. In particular, we
employ a recurrent neural network (LSTM) for adaptive feature extraction from
sequential data and a gradient boosting machinery (soft GBDT) for effective
supervised regression. Our framework is generic so that one can use other deep
learning architectures for feature extraction (such as RNNs and GRUs) and
machine learning algorithms for decision making as long as they are
differentiable. We demonstrate the learning behavior of our algorithm on
synthetic data and the significant performance improvements over the
conventional methods over various real life datasets. Furthermore, we openly
share the source code of the proposed method to facilitate further research
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization
We investigate nonlinear prediction/regression in an online setting and
introduce a hybrid model that effectively mitigates, via a joint mechanism
through a state space formulation, the need for domain-specific feature
engineering issues of conventional nonlinear prediction models and achieves an
efficient mix of nonlinear and linear components. In particular, we use
recursive structures to extract features from raw sequential sequences and a
traditional linear time series model to deal with the intricacies of the
sequential data, e.g., seasonality, trends. The state-of-the-art ensemble or
hybrid models typically train the base models in a disjoint manner, which is
not only time consuming but also sub-optimal due to the separation of modeling
or independent training. In contrast, as the first time in the literature, we
jointly optimize an enhanced recurrent neural network (LSTM) for automatic
feature extraction from raw data and an ARMA-family time series model (SARIMAX)
for effectively addressing peculiarities associated with time series data. We
achieve this by introducing novel state space representations for the base
models, which are then combined to provide a full state space representation of
the hybrid or the ensemble. Hence, we are able to jointly optimize both models
in a single pass via particle filtering, for which we also provide the update
equations. The introduced architecture is generic so that one can use other
recurrent architectures, e.g., GRUs, traditional time series-specific models,
e.g., ETS or other optimization methods, e.g., EKF, UKF. Due to such novel
combination and joint optimization, we demonstrate significant improvements in
widely publicized real life competition datasets. We also openly share our code
for further research and replicability of our results.Comment: Submitted to the IEEE TNNLS journa
A Pipeline for the ROTSE-IIId Archival Data
We have constructed a new, fast, robust and reliable pipeline to detect
variable stars from the ROTSE-IIId archival data. Turkish share of ROTSE-III
archive contains approximately one million objects from a large field of view
(1.85\dgr) and it considerably covers a large portion of northern sky
(\delta>-25\dgr). The unfiltered ROTSE-III magnitude of the objects ranges
from 7.7 to 16.9. The main stages of the new pipeline are as follows: Source
extraction, astrometry of the objects, light curve generation and inhomogeneous
ensemble photometry. A high performance computing (HPC) algorithm has also been
implemented into the pipeline where we had a good performance even on a
personal computer. Running the algorithms of the pipeline on a cluster
decreases analysis time significantly from weeks to hours. The pipeline is
especially tested against long period variable stars with periods of a few
hundred days (e.g Mira and SR) and variables having periods starting from a few
days to a few hundred days were detected.Comment: 8 pages, 5 figures 2 tables; last revision before publishe
Optimization of elastic spring supports for cantilever beams
Sönmez, Mustafa ( Aksaray, Yazar )In this study,a new approach of optimization algorithm is developed. The optimum distribution of elastic springs on which a cantilever Timoshenko beam is seated and minimization of the shear force on the support of the beam is investigated.The Fourier transform is applied to the beam vibration equation in the time domain and transfer function, independent from the external influence, is used to define the structural response. For all translational modes of the beam, the optimum locations and amounts of the springs are investigated so that the transfer function amplitude of the support shear force is minimized. The stiffness coefficients of the springs placed on the nodes of the beam divided into finite elements are considered as design variables. There is an active constraint on the sum of the spring coefficients taken as design variables and passive constraints on each of them as the upper and lower bounds. Optimality criteria are derived using the Lagrange Multipliers method. The gradient information required for solving the optimization problem is analytically derived. Verification of the new approach optimization algorithm was carried out by comparing the results presented in this paper with those ones from analysis of the model of the beam without springs, with springs with uniform stiffness and with optimal distribution of springs which support a cantilever beam to minimize the tip deflection of the beam found in the literature. The numerical results show that the presented method is effective in finding the optimum spring stiffness coefficients and location of springs for all translational modes.The proposed method can give designers an idea of how to support the cantilever beams under different harmonic vibrations