Optimal solutions for the online time series search and one-way trading problem with interrelated prices and a profit function

2018 ◽  
Vol 119 ◽  
pp. 465-471 ◽  
Author(s):  
Pascal Schroeder ◽  
Robert Dochow ◽  
Günter Schmidt
Keyword(s):  
Time Series ◽  
Profit Function ◽  
Optimal Solutions ◽  
Time Series Search

ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS

2009 ◽  
Vol 08 (04) ◽  
pp. 429-444 ◽  
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Chaotic Dynamics ◽  
Time Series Prediction ◽  
Data Series ◽  
Temporal Data ◽  
Optimal Solutions ◽  
Benchmark Data ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

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

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.

Time Series Search and Similarity Identification for Single Item Monitoring

2021 ◽  
pp. 479-487
Author(s):  
B. Denkena ◽  
B. Bergmann ◽  
J. Becker ◽  
T.-H. Stiehl
Keyword(s):  
Time Series ◽  
Single Item ◽  
Time Series Search

scholarly journals Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

2020 ◽  
Author(s):  
Filipe Gouveia ◽  
Inês Lynce ◽  
Pedro T. Monteiro
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Computational Models ◽  
Optimal Solutions ◽  
Biological Models ◽  
Logical Models ◽  
Manual Task ◽  
Cellular Processes ◽  
Biological Regulatory Networks ◽  
Boolean Regulatory Networks

AbstractMotivationComplex cellular processes can be represented by biological regulatory networks. Computational models of such networks have successfully allowed the reprodution of known behaviour and to have a better understanding of the associated cellular processes. However, the construction of these models is still mainly a manual task, and therefore prone to error. Additionally, as new data is acquired, existing models must be revised. Here, we propose a model revision approach of Boolean logical models capable of repairing inconsistent models confronted with time-series observations. Moreover, we account for both synchronous and asynchronous dynamics.ResultsThe proposed tool is tested on five well known biological models. Different time-series observations are generated, consistent with these models. Then, the models are corrupted with different random changes. The proposed tool is able to repair the majority of the corrupted models, considering the generated time-series observations. Moreover, all the optimal solutions to repair the models are produced.Contact{[email protected],[email protected]}

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