mlsanityData to 7 Oct 2026

Analytics of Business Time Series Using Machine Learning and Bayesian Inference

arXiv 2205.12905 ↗First posted 25 May 2022cs.LGcited by 1

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Abstract (arXiv)

In the survey we consider the case studies on sales time series forecasting, the deep learning approach for forecasting non-stationary time series using time trend correction, dynamic price and supply optimization using Q-learning, Bitcoin price modeling, COVID-19 spread impact on stock market, using social networks signals in analytics. The use of machine learning and Bayesian inference in predictive analytics has been analyzed.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 114 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

19 of 114 references.

References · 19

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7 more
  1. 2017Linear, Machine Learning and Probabilistic Approaches for Time Series Analysiscited by 5
  2. 2022Forming Predictive Features of Tweets for Decision-Making Supportcited by 5
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  5. 2022Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analyticscited by 2
  6. 2022Bitcoin Price Predictive Modeling Using Expert Correctioncited by 2
  7. 2022Forecasting of Non-Stationary Sales Time Series Using Deep Learningcited by 1

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Financial News Analytics Using Fine-Tuned Llama 2 GPT Modelcited by 6