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Bayesian non-parametric method for decision support: Forecasting online product sales
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2023 | Decision Support Systems | Citations: 0
Authors: Wu, Ziyue; Chen, Xi; Gao, Zhaoxing

Abstract: Forecasting online product sales is essential for retailers and e-commerce platf ... Expand

Abstract: Forecasting online product sales is essential for retailers and e-commerce platforms, but it can be challenging owing to the complex dynamics and mixed trends in sales data. Popular end-to-end approaches tend to capture spurious correlations in historical data, whereas two-stage approaches that decompose time series and make separate predictions often result in error accumulation. To address these issues, we propose PoissonGP, a novel Bayesian model that employs a non-homogeneous Poisson process with a Gaussian process prior for sales pre diction. PoissonGP can capture complex patterns in data with multiple trends and manage distribution shifts caused by changes in long-run sales. Additionally, it incorporates forecast uncertainty and provides interpret ability for building efficient and visual decision support systems. Experimental results on several synthetic and empirical datasets suggest that PoissonGP outperforms existing approaches, making it a promising tool for ecommerce platforms. Collapse

Semantic filters: seasonal autoregressive integrated moving average with exogenous regressor

Topics: marketing management digital platform user behavior online advertising electronic commerce

Methods: longitudinal research time series analysis experiment Bayesian analysis simulation
Predicting Hourly Bitcoin Prices Based on Long Short-term Memory Neural Networks

2021 | International Conference on Business Informatics | Citations: 0
Authors: Schulte, Maximilian; Eggert, Mathias

Abstract: Bitcoin is a cryptocurrency and is considered a high-risk asset class whose pric ... Expand

Abstract: Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %. Collapse

Semantic filters: seasonal autoregressive integrated moving average with exogenous regressor

Topics: Bitcoin Twitter Python Tether Google+

Methods: longitudinal research seasonal autoregressive integrated moving average with exogenous regressor artificial neural network descriptive statistic sentiment analysis

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Bayesian non-parametric method for decision support: Forecasting online product sales

Predicting Hourly Bitcoin Prices Based on Long Short-term Memory Neural Networks