Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network

• URL: http://arxiv.org/abs/2510.15900v1
• Date: Thu, 11 Sep 2025 03:14:55 GMT
• Title: Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network
• Authors: Emmanuel Boadi,
• Abstract summary: This study proposes a hybrid deep learning model for forecasting the price of Bitcoin.<n>The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast.

Related papers

• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• crypto price prediction using lstm+xgboost [0.0]
  This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction.<n>The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators.<n>The model is evaluated on historical datasets of Bitcoin, Dogecoin, and Litecoin, incorporating both global and localized exchange data.
  arXiv  Detail & Related papers  (2025-06-27T09:49:25Z)
• Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting [53.332533610841885]
  Time series can be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal.<n>These MMVs can reveal complementary patterns and enable the use of powerful pre-trained large models, such as large vision models (LVMs) for long-term time series forecasting (LTSF)<n>We propose DMMV, a novel decomposition-based multi-modal view framework that leverages trend-seasonal decomposition and a novel backcast-residual based adaptive decomposition to integrate MMVs for LTSF.
  arXiv  Detail & Related papers  (2025-05-29T20:55:24Z)
• A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices [0.0]
  We introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures.<n>By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting.<n>We evaluate the performance of our proposed model by comparing it with four other machine learning models.
  arXiv  Detail & Related papers  (2025-04-23T20:00:47Z)
• Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling [47.82616476928464]
  Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data.<n>We show that both training and sampling of MDMs are theoretically free from the time variable.<n>We identify, for the first time, an underlying numerical issue, even with the commonly used 32-bit floating-point precision.
  arXiv  Detail & Related papers  (2024-09-04T17:48:19Z)
• Comparative Study of Bitcoin Price Prediction [0.0]
  We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price.
  arXiv  Detail & Related papers  (2024-05-13T18:10:34Z)
• MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price [0.11470070927586015]
  We propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures.
  arXiv  Detail & Related papers  (2021-05-03T09:32:23Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market [62.997667081978825]
  We consider several hybrid modelling approaches for forecasting energy spot prices in EPEC market. Data was given in terms of electricity prices for 2013-2014 years, and test data as a year of 2015.
  arXiv  Detail & Related papers  (2020-10-14T12:45:53Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)
• Forecasting Bitcoin closing price series using linear regression and neural networks models [4.17510581764131]
  We study how to forecast daily closing price series of Bitcoin using data prices and volumes of prior days. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms.
  arXiv  Detail & Related papers  (2020-01-04T21:04:05Z)

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.