New algorithms for online time series search with interrelated prices

2019 ◽  
Author(s):  
Pascal Schroeder ◽  
Imed Kacem
Keyword(s):  
Time Series ◽  
Time Series Search ◽  
New Algorithms

Online algorithms for the multiple time series search problem

2012 ◽  
Vol 39 (5) ◽  
pp. 929-938 ◽  
Author(s):  
Wenming Zhang ◽  
Yinfeng Xu ◽  
Feifeng Zheng ◽  
Yucheng Dong
Keyword(s):  
Time Series ◽  
Online Algorithms ◽  
Search Problem ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Time Series Search

scholarly journals Modelling and (adaptive) control of greenhouse climates.

1984 ◽  
Vol 32 (2) ◽  
pp. 137-139 ◽  
Author(s):  
A.J. Udink ten Cate
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Time Series Analysis ◽  
Temperature Control ◽  
Control Process ◽  
Climate Control ◽  
Analysis Techniques ◽  
Series Analysis ◽  
Novel Approach ◽  
New Algorithms

After a discussion on control of greenhouse climates, new algorithms for temperature control are presented and tested in practice. A novel approach of modelling of the climate control process is presented by using time-series analysis techniques. (Abstract retrieved from CAB Abstracts by CABI’s permission)

Time Series Analysis

2014 ◽  
Author(s):  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
Alexander Gray ◽  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Applied Mathematics ◽  
Sampled Data ◽  
Series Analysis ◽  
Time Domain Data ◽  
Noise Data ◽  
Time Series Search ◽  
Modeling Toolkit

This chapter summarizes the fundamental concepts and tools for analyzing time series data. Time series analysis is a branch of applied mathematics developed mostly in the fields of signal processing and statistics. Contributions to this field, from an astronomical perspective, have predominantly focused on unevenly sampled data, low signal-to-noise data, and heteroscedastic errors. The chapter starts with a brief introduction to the main concepts in time series analysis. It then discusses the main tools from the modeling toolkit for time series analysis. Despite being set in the context of time series, many tools and results are readily applicable in other domains, and for this reason the examples presented will not be strictly limited to time-domain data. Armed with the modeling toolkit, the chapter goes on to discuss the analysis of periodic time series, search for temporally localized signals, and concludes with a brief discussion of stochastic processes.

New algorithms for processing time-series big EEG data within mobile health monitoring systems

2017 ◽  
Vol 149 ◽  
pp. 79-94 ◽  
Author(s):  
Mohamed Adel Serhani ◽  
Mohamed El Menshawy ◽  
Abdelghani Benharref ◽  
Saad Harous ◽  
Alramzana Nujum Navaz
Keyword(s):  
Time Series ◽  
Mobile Health ◽  
Health Monitoring ◽  
Processing Time ◽  
Monitoring Systems ◽  
Eeg Data ◽  
Health Monitoring Systems ◽  
Mobile Health Monitoring ◽  
New Algorithms

scholarly journals Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test

2008 ◽  
Vol 47 (9) ◽  
pp. 2423-2444 ◽  
Author(s):  
Xiaolan L. Wang
Keyword(s):  
Time Series ◽  
Open Source Software ◽  
Empirical Approach ◽  
Data Series ◽  
Climate Data ◽  
F Test ◽  
First Order ◽  
Open Source Software Package ◽  
Testing Algorithm ◽  
New Algorithms

Abstract This study proposes an empirical approach to account for lag-1 autocorrelation in detecting mean shifts in time series of white or red (first-order autoregressive) Gaussian noise using the penalized maximal t test or the penalized maximal F test. This empirical approach is embedded in a stepwise testing algorithm, so that the new algorithms can be used to detect single or multiple changepoints in a time series. The detection power of the new algorithms is analyzed through Monte Carlo simulations. It has been shown that the new algorithms work very well and fast in detecting single or multiple changepoints. Examples of their application to real climate data series (surface pressure and wind speed) are presented. An open-source software package (in R and FORTRAN) for implementing the algorithms, along with a user manual, has been developed and made available online free of charge.

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