A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm

George, Koshy; Mutalik, Prabhanjan

doi:10.1109/tsmc.2017.2712184

Cited by 33 publications

(9 citation statements)

References 57 publications

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“…However, mostly, they rely on mathematical equations, simulation and/or learning techniques to represent the evolution of time series data. The body of literature in time-series is devoted to time-series classification applications in machine learning and a wide range of industries [18]- [23].…”

Section: Related Workmentioning

confidence: 99%

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Danishvar¹,

Daneshvar²,

Mousavi³

2022

Preprint

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A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the DNA, repeatable and predictable.<br>

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Section: Related Workmentioning

confidence: 99%

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Danishvar¹,

Daneshvar²,

Mousavi³

2022

Preprint

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“…ML-based techniques are explored heavily as they can recognize complex patterns in stock prices [20]. Due to the nonlinear and time-varying nature of time-series, there has recently been a surge in demand for online prediction algorithms [21]. Online algorithms use the sequential calculation to achieve reliable and faster outcomes [13].…”

Section: Related Workmentioning

confidence: 99%

An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction

Mishra

Ahmed

Mishra

et al. 2022

Scientific Programming

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The idea of multivariate and online stock price prediction via the kernel adaptive filtering (KAF) paradigm is proposed in this article. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. This is problematic considering the nonstationary nature of a financial time series. In this research, we propose an online kernel adaptive filtering-based approach for stock price prediction to overcome this challenge. To examine a stock's performance and demonstrate the work's superiority, we use ten different KAF family of algorithms. In this paper, we take on this challenge and propose an approach for predicting stock prices. To analyze a stock's performance and demonstrate the work's superiority, we use ten distinct KAF algorithms. Besides, the results are analyzed on nine-time windows such as one day, sixty minutes, thirty minutes, twenty five minutes, twenty minutes, fifteen minutes, ten minutes, five minutes, and one minute. We are the first to experiment with several time windows for all fifty stocks on the Indian National Stock Exchange, to the best of our knowledge. It should be noted here that the experiments are performed on stocks making up the main index: Nifty-50. In terms of performance and compared to existing methods, we have a 66% probability of correctly predicting a stock's next upward or downward movement. This number clearly shows the edge that the proposed method has in actual deployment. Furthermore, the experimental findings show that KAF is not only a better option for predicting stock prices but that it may also be used as an alternative in high-frequency trading due to its low latency.

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“…For example, when the threshold of ϕ increases from 0.1 to 0.8, the confidence of MTSPPR gradually rises from 0.68 to 0.90 in the case of SST, from 0.72 to 0.87 for MET, from 0.65 to 0.85 for TF, and from 0.35 to 0.45 for SP. According to Equation (16), an increase in the threshold of ϕ means more candidate comparison subsequences with different lengths are involved to a given subsequence in each operation. Frankly speaking, while facilitating the accuracy of periodicity detection, the increase of the ϕ threshold will also lead to an increase in computational overhead.…”

Section: Impact Of Thresholds Of Parameters µ and ϕmentioning

confidence: 99%

A periodicity-based parallel time series prediction algorithm in cloud computing environments

Chen

Rong

et al. 2019

Information Sciences

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In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data. It is critical to efficiently identify the potential periodic patterns from massive time-series data and provide accurate predictions. In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment. To effectively handle the massive historical datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is presented, which can reduce the data scale as well as accurately extracting the characteristics. Based on this, we propose a Multi-layer Time Series Periodic Pattern Recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis (FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP) algorithm is proposed. Data in the subsequent period are predicted based on all previous period models, in which a time attenuation factor is introduced to control the impact of different periods on the prediction results. Moreover, to improve the performance of the proposed algorithms, we propose a parallel solution on the Apache Spark platform, using the Streaming real-time computing module. To efficiently process the large-scale time-series datasets in distributed computing environments, Distributed Streams (DStreams) and Resilient Distributed Datasets (RDDs) are used to store and calculate these datasets. Logical and data dependencies of RDDs in the P-TSDCA, P-MTSPPR, and P-PTSP processes are considered, and the corresponding parallel execution solutions are conducted. Extensive experimental results show that our PPTSP algorithm has significant advantages compared with other algorithms in terms of prediction accuracy and performance.

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A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm

Cited by 33 publications

References 57 publications

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction

A periodicity-based parallel time series prediction algorithm in cloud computing environments

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