scholarly journals Clustering of Latvian Pension Funds Using Convolutional Neural Network Extracted Features

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2086
Author(s):  
Vitalija Serapinaitė ◽  
Audrius Kabašinskas
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stock Prices ◽  
Pension Fund ◽  
Time Series Data ◽  
Clustering Algorithms ◽  
Pension Funds ◽  
Series Data ◽  
Clustering Methods ◽  
Poverty Level

Pension funds became a fundamental part of financial security in pensioners’ lives, guaranteeing stable income throughout the years and reducing the chance of living below the poverty level. However, participating in a pension accumulation scheme does not ensure financial safety at an older age. Various pension funds exist that result in different investment outcomes ranging from high return rates to underperformance. This paper aims to demonstrate alternative clustering of Latvian second pillar pension funds, which may help system participants make long-range decisions. Due to the demonstrated ability to extract meaningful features from raw time-series data, the convolutional neural network was chosen as a pension fund feature extractor that was used prior to the clustering process. In this paper, pension fund cluster analysis was performed using trained (on daily stock prices) convolutional neural network feature extractors. The extractors were combined with different clustering algorithms. The feature extractors operate using the black-box principle, meaning the features they learned to recognize have low explainability. In total, 32 models were trained, and eight different clustering methods were used to group 20 second-pillar pension funds from Latvia. During the analysis, the 12 best-performing models were selected, and various cluster combinations were analyzed. The results show that funds from the same manager or similar performance measures are frequently clustered together.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

scholarly journals Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

2020 ◽  
Vol 11 (1) ◽  
pp. 77
Author(s):  
Jaehyeon Nam ◽  
Jaeyoung Kang
Keyword(s):  
Neural Network ◽  
Lyapunov Exponent ◽  
Dynamic System ◽  
Convolutional Neural Network ◽  
Dynamic Characteristics ◽  
Time Series Data ◽  
Fundamental Property ◽  
Recurrence Plot ◽  
Series Data ◽  
Nonsmooth Problems

This study classified chaotic time series data, including smooth and nonsmooth problems in a dynamic system, using a convolutional neural network (CNN) and verified it through the Lyapunov exponent. For this, the classical nonlinear differential equation by the Lorenz model was used to analyze a smooth dynamic system. The vibro-impact model was used for the nonsmooth dynamic system. Recurrence is a fundamental property of a dynamic system, and a recurrence plot is a representative method to visualize the recurrence characteristics of reconstructed phase space. Therefore, this study calculated the Lyapunov exponent by parametric analysis and visualized the corresponding recurrence matrix to show the dynamic characteristics as an image. In addition, the dynamic characteristics were classified using the proposed CNN model. The proposed CNN model determined chaos with an accuracy of more than 92%.

scholarly journals Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1758
Author(s):  
Shangyi Yang ◽  
Chao Sun ◽  
Youngok Kim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Data ◽  
Indoor Navigation ◽  
Series Data ◽  
Positioning Accuracy ◽  
3D Localization ◽  
Localization Scheme ◽  
3D Positioning

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

scholarly journals Vibration Anomaly Detection using Deep Neural Network and Convolutional Neural Network

2021 ◽  
pp. 19-28
Author(s):  
Crina Deac ◽  
◽  
Gicu Călin Deac ◽  
Radu Constantin Parpală ◽  
Cicerone Laurentiu Popa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
Health State ◽  
Optimal Learning ◽  
Accuracy And Precision ◽  
The Cost

Identifying the “health state” of the equipment is the domain of condition monitoring. The paper proposes a study of two models: DNN (Deep Neural Network) and CNN (Convolutional Neural Network) over an existent dataset provided by Case Western Reserve University for analyzing vibrations in fault diagnosis. After the model is trained on the windowed dataset using an optimal learning rate, minimizing the cost function, and is tested by computing the loss, accuracy and precision across the results, the weights are saved, and the models can be tested on other real data. The trained model recognizes raw time series data collected by micro electro-mechanical accelerometer sensors and detects anomalies based on former times series entries.

scholarly journals Low-dimensional Convolutional Neural Network for Solar Flares GOES Time-series Classification

2022 ◽  
Vol 258 (1) ◽  
pp. 12
Author(s):  
Vlad Landa ◽  
Yuval Reuveni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Solar Flare ◽  
Convolutional Neural Network ◽  
Solar Flares ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
X Ray ◽  
Low Dimensional

Abstract Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at https://github.com/vladlanda.

scholarly journals Similarity Preserving Representation Learning for Time Series Clustering

2019 ◽  
Author(s):  
Qi Lei ◽  
Jinfeng Yi ◽  
Roman Vaculin ◽  
Lingfei Wu ◽  
Inderjit S. Dhillon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Clustering Algorithms ◽  
Representation Learning ◽  
Feature Representation ◽  
Series Data ◽  
Similarity Matrix ◽  
Clustering Methods ◽  
Time Series Clustering ◽  
Partially Observed

A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation , thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we demonstrate that the proposed framework is much more effective, efficient, and flexible compared to other state-of-the-art clustering methods.

scholarly journals A lightweight double-channel depthwise separable convolutional neural network for multimodal fusion gait recognition

2021 ◽  
Vol 19 (2) ◽  
pp. 1195-1212
Author(s):  
Xiaoguang Liu ◽  
◽  
Meng Chen ◽  
Tie Liang ◽  
Cunguang Lou ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Data ◽  
Gait Recognition ◽  
Wearable Devices ◽  
Multimodal Fusion ◽  
Model Complexity ◽  
Series Data ◽  
Double Channel

<abstract> <p>Gait recognition is an emerging biometric technology that can be used to protect the privacy of wearable device owners. To improve the performance of the existing gait recognition method based on wearable devices and to reduce the memory size of the model and increase its robustness, a new identification method based on multimodal fusion of gait cycle data is proposed. In addition, to preserve the time-dependence and correlation of the data, we convert the time-series data into two-dimensional images using the Gramian angular field (GAF) algorithm. To address the problem of high model complexity in existing methods, we propose a lightweight double-channel depthwise separable convolutional neural network (DC-DSCNN) model for gait recognition for wearable devices. Specifically, the time series data of gait cycles and GAF images are first transferred to the upper and lower layers of the DC-DSCNN model. The gait features are then extracted with a three-layer depthwise separable convolutional neural network (DSCNN) module. Next, the extracted features are transferred to a softmax classifier to implement gait recognition. To evaluate the performance of the proposed method, the gait dataset of 24 subjects were collected. Experimental results show that the recognition accuracy of the DC-DSCNN algorithm is 99.58%, and the memory usage of the model is only 972 KB, which verifies that the proposed method can enable gait recognition for wearable devices with lower power consumption and higher real-time performance.</p> </abstract>

scholarly journals The impact of pension systems on financial development: An empirical study

2014 ◽  
Vol 4 (3) ◽  
pp. 119-130 ◽  
Author(s):  
Shouji Sun ◽  
Jiye Hu
Keyword(s):  
Financial Development ◽  
Pension Fund ◽  
Time Series Data ◽  
Pension System ◽  
Panel Data Analysis ◽  
Pension Funds ◽  
Series Data ◽  
Legal Origin ◽  
Pension Systems ◽  
The Impact

The impact of pension assets on financial development is both quantitatively and qualitatively. On quantitatively, pension funds increase capital supply to financial market. On qualitatively, pension funds as institutional investors could promote corporate governance, information disclosure and transaction efficiency. Based on regression results of 55 countries and regions, we find that different pension systems formed different size of pension fund; every 1% increase of the pension funds’ assets could bring about 0.15%-0.23% increase of the market value, which could explain cross-countries difference of financial development. Based on panel data analysis, we find that the impact of pension fund on financial development is very significant especially in civil law and underdeveloped countries. By using co-integration analysis and vector auto regression model (VAR) with time series data of Chile, we find positive relationship between pension funds and financial development again. The empirical results indicate that legal origin, endowment and pension fund views are not exclusive but compatible. A country cannot change its legal origin and endowment, but it can change pension policies and reform social security system. A funded pension system with accumulates pension assets could promote a country’s financial development and economic growth.

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