Adaptive Time Series Momentum: An Objective Benchmark for Systematic Trend-Following Strategies

2016 ◽  
Author(s):  
Gert Elaut
Keyword(s):  
Time Series ◽  
Systematic Trend ◽  
Adaptive Time

scholarly journals Alternate Structures for Adaptive Time Series Modelling

1987 ◽  
Vol 20 (2) ◽  
pp. 369-374
Author(s):  
D.C. Farden ◽  
J.R. Bellegarda
Keyword(s):  
Time Series ◽  
Time Series Modelling ◽  
Adaptive Time

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

An Adaptive Time-Series Probabilistic Framework for 4-D Trajectory Conformance Monitoring

2016 ◽  
Vol 17 (6) ◽  
pp. 1606-1616 ◽  
Author(s):  
Dimitrios Sotiriou ◽  
Fotis Kopsaftopoulos ◽  
Spilios Fassois
Keyword(s):  
Time Series ◽  
Probabilistic Framework ◽  
Adaptive Time

scholarly journals Detection of relevant information in intrinsic mode functions

2020 ◽  
Vol 14 (1) ◽  
pp. 102-110
Author(s):  
Roberto Sebastián Hernández Santander ◽  
Esperanza Camargo Casallas
Keyword(s):  
Time Series ◽  
Relevant Information ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Levels Of Detail ◽  
Mode Decomposition ◽  
Fm Signals ◽  
Finite Set ◽  
Tunnel Syndrome ◽  
Adaptive Time

The empirical mode decomposition (EMD) decomposes a local and adaptive time series into a finite set of intrinsic mode functions (IMF), AM-FM signals that allow to represent a non-linear and non-stationary model with the advantage of not losing the underlying meaning. This study examines time series of sEMG measurements for a case study of healthy individuals with carpal tunnel syndrome. Due to the amount of multiple levels of detail, all around a central frequency and evoked by the number of IMFs obtained through EMD, the informational contribution of each at the intermodal and interindividual level is studied through Shannon entropy to establish a general framework of spectral study given Hilbert Huang's (HHT) transformation to remarkable degrees of information. The results show that the latest IMFs have more disordered states even when they engage in apparently regular behavior, agglomerate more time-frequency information, and in the same way, concentrate more differentiable characteristics for a process of individualization of patterns.

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