Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on–off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

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Case Study 15—Determining Lost Sales with Stationary Time Series Data

A Quantitative Approach to Commercial Damages ◽

10.1002/9781119202646.ch15 ◽

2012 ◽

pp. 251-272

Keyword(s):

Time Series ◽

Time Series Data ◽

Stationary Time Series ◽

Series Data ◽

Lost Sales ◽

Stationary Time

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ANALISIS PERMINTAAN TENAGA KERJA PADA PERUSAHAAN PERKEBUNAN SAWIT (Studi Kasus PT.SOCFINDO Perkebunan Seunagan Kabupaten Nagan Raya)

Jurnal Bisnis Dan Kajian Strategi Manajemen ◽

10.35308/jbkan.v2i1.892 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Leli Putri Ansari

Keyword(s):

Time Series ◽

Data Analysis ◽

Significant Influence ◽

Oil Palm ◽

Time Series Data ◽

Secondary Data ◽

Series Data ◽

Analysis Model ◽

Simultaneous Testing

AbstractThis study aims to analyze the effect of wages and production on oil palm plantation companies, a case study of PT.Socfindo Seunagan Nagan Raya district. This research methode uses multiple liniear regression data analysis model. This research is quantitative and time series data for the period of 2005-2016 and data in the form of secondary data obtained from PT.Socfindo Seunagan Nagan Raya district and Central Bureau of statistics (BPS) Nagan Raya district.Based on the results of research partial testing that wages have a significant influence on labor for demand. Where as production has no significant effect on the demand for labor. Simultaneous testing that wages and production effect labor for demand Keyword: Wage, production, and labor for demand

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Time Series Data Mining: A Case Study With Big Data Analytics Approach

IEEE Access ◽

10.1109/access.2020.2966553 ◽

2020 ◽

Vol 8 ◽

pp. 14322-14328 ◽

Cited By ~ 3

Author(s):

Fang Wang ◽

Menggang Li ◽

Yiduo Mei ◽

Wenrui Li

Keyword(s):

Data Mining ◽

Time Series ◽

Big Data ◽

Data Analytics ◽

Time Series Data ◽

Big Data Analytics ◽

Series Data ◽

Time Series Data Mining

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

The Pakistan Development Review ◽

10.30541/v8i2pp.308-309 ◽

1968 ◽

Vol 8 (2) ◽

pp. 308-309

Author(s):

Mohammad Irshad Khan

Keyword(s):

New York ◽

Time Series ◽

Time Series Data ◽

Publishing House ◽

Economic Incentives ◽

The Other ◽

Series Data ◽

Poor Countries ◽

Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

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Tourism Development and Economic Growth Nexus in Malawi - A Time-Series Data Analysis, 1985 - 2015

SSRN Electronic Journal ◽

10.2139/ssrn.3705965 ◽

2020 ◽

Author(s):

Lloyd George Banda

Keyword(s):

Economic Growth ◽

Time Series ◽

Data Analysis ◽

Time Series Data ◽

Tourism Development ◽

Series Data ◽

Time Series Data Analysis

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Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

Bulletin of Mathematical Biology ◽

10.1007/s11538-020-00847-3 ◽

2021 ◽

Vol 83 (3) ◽

Author(s):

Maria-Veronica Ciocanel ◽

Riley Juenemann ◽

Adriana T. Dawes ◽

Scott A. McKinley

Keyword(s):

Time Series ◽

Data Analysis ◽

Actin Filaments ◽

Time Series Data ◽

Polymer Networks ◽

Topological Data Analysis ◽

Series Data ◽

Topological Features ◽

Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

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Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽

10.3390/ai2010005 ◽

2021 ◽

Vol 2 (1) ◽

pp. 48-70

Author(s):

Wei Ming Tan ◽

T. Hui Teo

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Remaining Useful Life ◽

Sensor Data ◽

Series Data ◽

Multiple Time ◽

Data Set ◽

Form Complex ◽

Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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