Mining frequent pyramid patterns from time series transaction data with custom constraints

Wang, Wei; Jing, Tian; Lv, Fang; Xin, Guodong; Ma, Yingfan; Wang, Bailing

doi:10.1016/j.cose.2020.102088

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…Li et al [18] proposed a one-pass algorithm, with a commonly used frequency as the output for a given sequence, and two advanced models to further improve the processing efficiency of voluminous sequences and streaming data. To address the shortcomings of traditional sequence pattern mining algorithms, Wang et al [19] pro-posed a timeliness variable threshold and an increment Prefixspan algorithm, verifying the effectiveness of the algorithm. Sophisticated investors can analyze candlestick sequences in historical data and speculate on the patterns that will appear in the next period, thus enabling them to prognosticate future trends in stock markets.…”

Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

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Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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“…Thus, the economic essence of financial pyramids remains unchanged: the incomes of some investors are formed not due to real investments but due to cash inflows from other participants in the pyramid. W. Wang et al (2021) note that the incomes received by the organisers and participants of the financial pyramid, including the first investors who invested money and managed to receive profits, are an intermediate result. In other words, all participants cannot expect positive results from such a "scheme": at best, they can preserve their initial investments, but most often the pyramid structure is organised in such a way that initial contributors receive more than they invested, leaving the latter with nothing.…”

Section: The Essence Of Crimes Related To the Creation And Management...mentioning

confidence: 99%

Criminal Legal Measures to Counteract the Activities of Financial Pyramids in the Republic of Kazakhstan

2024

PJC

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The purpose of this study is to examine ways to counteract the activities of financial pyramids. To achieve this purpose, the study employs methods such as logical analysis, formal-legal analysis, dogmatic analysis, legal hermeneutics, deduction, induction. The study defines the concept of a “financial pyramid,” identifies its characteristics, signs, implementation principles, role, and influence on the socio-economic life of the state. Statistical data from the Republic of Kazakhstan were analysed, leading to the conclusion that the number of criminal activities in this category increases annually. Moreover, regions where these illegal activities are most prevalent were identified. In this regard, an analysis of Kazakhstan’s legislative framework was conducted, specifically the Criminal Code of 1997 and 2014, highlighting key problematic aspects and normative-legal conflicts. A comparative legal analysis was also conducted, comparing the experiences of countries such as the USA, Poland, Germany, among others.

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“…Streaming data mining includes techniques for frequent item set detection, where the process is adjusted because of dynamics in data streams [20][21][22]. e main problem with mining frequent items in data streams is to find a compatible model for dynamically storing the data and, in parallel, applying all operations for determining frequent itemsets in efficient time.…”

Section: Related Workmentioning

confidence: 99%

LCTree-Based Approach for Mining Frequent Items in Real-Time

Chen

Zhong

et al. 2022

Computational Intelligence and Neuroscience

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With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications.

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Mining frequent pyramid patterns from time series transaction data with custom constraints

Cited by 13 publications

References 30 publications

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Criminal Legal Measures to Counteract the Activities of Financial Pyramids in the Republic of Kazakhstan

LCTree-Based Approach for Mining Frequent Items in Real-Time

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