Browsing by Subject "Time series prediction"

Now showing 1 - 4 of 4

Open Access
Deep fractional Fourier networks
(2024-08) Koç, Emirhan
This thesis introduces the integration of the fractional Fourier Transform (FrFT) into the deep learning domain, with the aim of opening new avenues for incorporating signal processing into deep neural networks (DNNs). This work starts by introducing FrFT into recurrent neural networks (RNNs) for time series prediction, leveraging its ability and flexibility to perform infinitely many continuous transformations and offering an alternative to the traditional Fourier Transform (FT). Despite the initial success, a significant challenge identified is the manual tuning of the fraction order parameter a, which can be cumbersome and limits broader applicability. To overcome this limitation, we introduce a novel approach where the fraction order a is treated as a learnable parameter within deep learning models. First, a theoretical foundation is established to support the learnability of this parameter, followed by extensive experimentation in image classification and time series prediction tasks. The results demonstrate that incorporating a learnable fraction order significantly improves model performance, particularly when integrated with well-known architectures such as ResNet and VGG models. Furthermore, the thesis proposes fractional Fourier Pooling (FrFP), a pooling technique that replaces traditional Global Average Pooling (GAP) layers in Convolutional Neural Networks (CNNs). FrFP enhances feature representation by processing intermediate signal regions, leading to better model performance and offering a new perspective on integrating signal transformations within deep learning frameworks. Overall, this thesis contributes to the growing body of research exploring advanced signal processing techniques in deep learning, highlighting the potential of FrFT as a powerful tool for improving model accuracy and efficiency across various applications.
Open Access
Markovian RNN: an adaptive time series prediction network with HMM-based switching for nonstationary environments
(Institute of Electrical and Electronics Engineers Inc., 2023-02-01) İlhan, Fatih; Karaahmetoğlu, Oğuzhan; Balaban, İ.; Kozat, Süleyman Serdar
We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy, and economy, time series data exhibit nonstationarity due to the temporally varying dynamics of the underlying system. We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. Our model, Markovian RNN employs a hidden Markov model (HMM) for regime transitions, where each regime controls hidden state transitions of the recurrent cell independently. We jointly optimize the whole network in an end-to-end fashion. We demonstrate the significant performance gains compared to conventional methods such as Markov Switching ARIMA, RNN variants and recent statistical and deep learning-based methods through an extensive set of experiments with synthetic and real-life datasets. We also interpret the inferred parameters and regime belief values to analyze the underlying dynamics of the given sequences.
Open Access
Markovian RNN: an adaptive time series prediction network with HMM-based switching for nonstationary environments
(Institute of Electrical and Electronics Engineers, 2021-08-09) İlhan, Fatih; Karaahmetoğlu, Oğuzhan; Balaban, İ.; Kozat, Süleyman Serdar
We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy, and economy, time series data exhibit nonstationarity due to the temporally varying dynamics of the underlying system. We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. Our model, Markovian RNN employs a hidden Markov model (HMM) for regime transitions, where each regime controls hidden state transitions of the recurrent cell independently. We jointly optimize the whole network in an end-to-end fashion. We demonstrate the significant performance gains compared to conventional methods such as Markov Switching ARIMA, RNN variants and recent statistical and deep learning-based methods through an extensive set of experiments with synthetic and real-life datasets. We also interpret the inferred parameters and regime belief values to analyze the underlying dynamics of the given sequences.
Open Access
Nonstationary time series prediction with Markovian switching recurrent neural networks
(2021-07) İlhan, Fatih
We investigate nonlinear prediction for nonstationary time series. In most real-life scenarios such as ﬁnance, retail, energy and economy applications, time se-ries data exhibits nonstationarity due to the temporally varying dynamics of the underlying system. This situation makes the time series prediction challenging in nonstationary environments. We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. Our model, Markovian RNN employs a hidden Markov model (HMM) for regime transitions, where each regime controls hidden state transitions of the recurrent cell inde-pendently. We jointly optimize the whole network in an end-to-end fashion. We demonstrate the signiﬁcant performance gains compared to conventional methods such as Markov Switching ARIMA, RNN variants and recent statistical and deep learning-based methods through an extensive set of experiments with synthetic and real-life datasets. We also interpret the inferred parameters and regime belief values to analyze the underlying dynamics of the given sequences.