Feature-Based Clustering for Electricity Use Time Series Data

2009 ◽  
pp. 401-412 ◽  
Author(s):  
Teemu Räsänen ◽  
Mikko Kolehmainen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Electricity Use ◽  
Feature Based

scholarly journals CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

2021 ◽  
Vol V-1-2021 ◽  
pp. 31-38
Author(s):  
M. Arnold ◽  
M. Hoyer ◽  
S. Keller
Keyword(s):  
Time Series ◽  
Event Detection ◽  
Time Series Data ◽  
Radar Data ◽  
Data Driven ◽  
Series Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Feature Based ◽  
Bridge Crossing

Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. To sum up, the presented results reveal the potential of a data-driven DL approach concerning the detection of bridge crossing events in GBR-based displacement time series data. Based on such an event detection, a prospective assessment of bridge conditions seems feasible as an extension to previous structural health monitoring approaches.

scholarly journals Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 27760-27776 ◽  
Author(s):  
Min Hu ◽  
Zhiwei Ji ◽  
Ke Yan ◽  
Ye Guo ◽  
Xiaowei Feng ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Feature Based

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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