Asset price movement prediction using empirical mode decomposition and Gaussian mixture models

• URL: http://arxiv.org/abs/2503.20678v1
• Date: Wed, 26 Mar 2025 16:12:11 GMT
• Title: Asset price movement prediction using empirical mode decomposition and Gaussian mixture models
• Authors: Gabriel R. Palma, Mariusz Skoczeń, Phil Maguire,
• Abstract summary: We used five, two, and one year samples of hourly candle data for GameStop, Tesla, andRipple markets.<n>We collected several features based on a linear model and other classical features to predict the next hour's movement.<n>We evaluated the performance of various machine learning models, including Random Forests (RF) and XGBoost, in classifying market movements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigated the use of Empirical Mode Decomposition (EMD) combined with Gaussian Mixture Models (GMM), feature engineering and machine learning algorithms to optimize trading decisions. We used five, two, and one year samples of hourly candle data for GameStop, Tesla, and XRP (Ripple) markets respectively. Applying a 15 hour rolling window for each market, we collected several features based on a linear model and other classical features to predict the next hour's movement. Subsequently, a GMM filtering approach was used to identify clusters among these markets. For each cluster, we applied the EMD algorithm to extract high, medium, low and trend components from each feature collected. A simple thresholding algorithm was applied to classify market movements based on the percentage change in each market's close price. We then evaluated the performance of various machine learning models, including Random Forests (RF) and XGBoost, in classifying market movements. A naive random selection of trading decisions was used as a benchmark, which assumed equal probabilities for each outcome, and a temporal cross-validation approach was used to test models on 40%, 30%, and 20% of the dataset. Our results indicate that transforming selected features using EMD improves performance, particularly for ensemble learning algorithms like Random Forest and XGBoost, as measured by accumulated profit. Finally, GMM filtering expanded the range of learning algorithm and data source combinations that outperformed the top percentile of the random baseline.

Related papers

• Combining supervised and unsupervised learning methods to predict financial market movements [0.0]
  We identify novel features extracted from emergent and well-established financial markets. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements.
  arXiv  Detail & Related papers  (2024-08-19T13:17:36Z)
• Feature selection and regression methods for stock price prediction using technical indicators [0.0]
  This study uses technical indicators and features selection and regression methods to solve the problem of closing the stock market price. The use of suitable combination of suggested indicators along with regression methods has resulted in high accuracy in predicting the closing price.
  arXiv  Detail & Related papers  (2023-10-15T18:09:09Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data [0.0]
  We introduce an innovative algorithm that evaluates cluster effects for new data points, thereby increasing the robustness and precision of this model. The inferential and predictive efficacy of this approach is further illustrated through its application in student scoring and protein expression.
  arXiv  Detail & Related papers  (2023-02-06T23:34:51Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Nowcasting the Financial Time Series with Streaming Data Analytics under Apache Spark [3.219821135628767]
  This paper proposes nowcasting of high-frequency financial datasets in real-time with a 5-minute interval using the streaming analytics feature of Apache Spark. The proposed 2 stage method consists of modelling chaos in the first stage and then using a sliding window approach for training with machine learning algorithms.
  arXiv  Detail & Related papers  (2022-02-23T23:17:01Z)
• Comparative Study of Machine Learning Models for Stock Price Prediction [0.0]
  We apply machine learning techniques to historical stock prices to forecast future prices. We quantify the results by computing the error of the predicted values versus the historical values of each stock. This method could be used to automate portfolio generation for a target return rate.
  arXiv  Detail & Related papers  (2022-01-31T17:16:27Z)
• Cauchy-Schwarz Regularized Autoencoder [68.80569889599434]
  Variational autoencoders (VAE) are a powerful and widely-used class of generative models. We introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs. Our objective improves upon variational auto-encoding models in density estimation, unsupervised clustering, semi-supervised learning, and face analysis.
  arXiv  Detail & Related papers  (2021-01-06T17:36:26Z)
• An Adaptive EM Accelerator for Unsupervised Learning of Gaussian Mixture Models [0.7340845393655052]
  We propose an Anderson Acceleration scheme for the adaptive Expectation-Maximization (EM) algorithm for unsupervised learning. The proposed algorithm is able to determine the optimal number of mixture components autonomously, and converges to the optimal solution much faster than its non-accelerated version.
  arXiv  Detail & Related papers  (2020-09-26T22:55:44Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.