Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of…
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Long Short-Term Memory (LSTM) is a well-known method used widely on sequence learning and time series prediction. In this paper we deployed stacked LSTM model in an application of weather forecasting. We propose a 2-layer spatio-temporal stacked LSTM model which consists of independent LSTM models per location in the f…
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
problem Improving accuracy in time series prediction.
method Combines difference-attention LSTM and error-correction LSTM in a cascade approach.
result Improves prediction accuracy in time series.
Simplified LSTM models improve sentiment analysis on Twitter debate data.
problem Performing sentiment analysis on long sequence data from Twitter debates.
method Developed six parameter-reduced LSTM models (slim LSTM) for faster training and reduced computational cost.
result Slim LSTM models outperform standard LSTM model in sentiment analysis of GOP Debate Twitter dataset.
Enhanced LSTM model learns complex temporal dependencies.
problem Modeling long-term dependencies in sequential data.
method Gamma-LSTM with hierarchical memory units and gates.
result Gamma-LSTM outperforms regular and stacked LSTMs in sequence prediction.
While LSTMs show increasingly promising results for forecasting Financial Time Series (FTS), this paper seeks to assess if attention mechanisms can further improve performance. The hypothesis is that attention can help prevent long-term dependencies experienced by LSTM models. To test this hypothesis, the main contribu…
BiLSTM models outperform LSTM and ARIMA in financial time series forecasting.
problem Comparing forecasting accuracy of ARIMA, LSTM, and BiLSTM models.
method Behavioral analysis and comparison of BiLSTM and LSTM models.
result BiLSTM models provide better predictions than ARIMA and LSTM models.
Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, we propose a hidden-laye…
MC-LSTM extends LSTM to conserve mass in neural networks.
problem Conservation laws in real-world systems.
method Extending LSTM's inductive bias to conserve mass.
result MC-LSTM sets new state-of-the-art for predicting peak flows.
Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks. RNNs are competent in understanding temporal dependencies. The use of LSTM cells fu…
LSTM models improve macroeconomic forecasting with mixed frequency data.
problem Improving accuracy of macroeconomic forecasts using mixed frequency data.
method Adapted LSTM model to mixed frequency data, using U-MIDAS scheme.
result Proposed LSTM models outperform conventional MIDAS models in out-of-sample predictive performance.
Study predicts cryptocurrency trends using LSTM model.
problem Predicting cryptocurrency price trends.
method Combination of window-sliding and prediction range method with LSTM model.
result Established model for cryptocurrency price trend prediction.
Paper questions RNN and LSTM's long-term memory and introduces a new definition.
problem Whether RNN and LSTM have long-term memory.
method Introduced a new definition of long-term memory and modified RNN and LSTM to test it.
result RNN and LSTM do not meet the new definition of long-term memory.
Variational Bi-LSTMsstat.ML
Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are gene…
Hybrid model forecasts Bitcoin prices better than standard LSTM.
problem Forecasting Bitcoin price fluctuations.
method VMD for decomposition, LSTM for modeling IMFs, final prediction aggregation.
result Hybrid model outperforms standard LSTM in various metrics.
Study compares LSTM and Transformer models in financial time series prediction.
problem Comparing LSTM and Transformer models for financial time series prediction.
method Various LSTM-based and Transformer-based models compared on financial tasks; DLSTM and new Transformer architecture designed.
result Transformer-based models show limited advantage in absolute price sequence prediction, while LSTM-based models perform better on difference sequences.
LSTMs improve bond yield forecasting with unique signals.
problem Improving bond yield forecasting accuracy.
method Long short-term memory (LSTM) networks with sequence-to-sequence architectures and LSTM-LagLasso methodology.
result Univariate LSTM models with additional memory can achieve similar results as multivariate MLP models using exogenous information.
DALC customizes LSTM models for detectors in large-scale traffic networks.
problem Fine-grained traffic prediction for large-scale transportation networks.
method Formulated as a finite Markov decision process, introduced ALC algorithm for automatic customization, and developed DALC for distributed customization.
result DALC provides higher prediction accuracy than Apache Spark MLlib approaches.
Paper proposes using LSTM for LSH-based sequence alignment.
problem Sequence alignment using deep learning models.
method Deep bidirectional LSTM for feature learning and LSH-based sequence alignment.
result Higher accuracy achieved with LSTM-based model.
In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to…
Extended LSTM improves language modeling performance.
problem Improving LSTM for large-scale language modeling.
method Exponential gating, modified memory structure, and residual stacking.
result xLSTM outperforms state-of-the-art models in performance and scaling.
DP-LSTM predicts stock prices using financial news with improved accuracy and privacy.
problem Predicting stock prices with financial news articles.
method Integrates financial news articles into a sentiment-ARMA model, then uses an LSTM network with differential privacy.
result Achieves up to 65.79% improvement in MSE for S&P 500 prediction.
Hybrid model outperforms benchmarks in financial forecasting.
problem Robust asset price forecasting in finance.
method Combining LSTM with Neural Levy Processes using Grey Wolf Optimizer and ANN calibration.
result Hybrid model outperforms base LSTM and other models.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better p…
Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art performance on the task of classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why L…
LSTM model predicts stock returns with over 90% accuracy.
problem Predicting future stock market prices and returns is challenging.
method Used Long Short-Term Memory (LSTM) model trained on historical NSE data.
result LSTM model achieved over 90% accuracy in predicting stock prices and returns.
Language models based on deep neural networks and traditional stochastic modelling have become both highly functional and effective in recent times. In this work, a general survey into the two types of language modelling is conducted. We investigate the effectiveness of the Hidden Markov Model (HMM), and the Long Short…
The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Featuresq-fin.ST
Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
problem Improving interpretability of LSTM models for predicting oil company stocks.
method Designed and trained Standard LSTM networks using various correlated datasets.
result Adding correlated features does not enhance LSTM model interpretability.
Paper applies FloatSD8 to LSTM networks, reducing complexity and power.
problem Training and inference complexity of LSTM networks.
method Applied FloatSD8 for weights, 8-bit quantization for gradients/activations, reduced arithmetic precision.
result Successfully trained LSTM models with reduced complexity and preserved accuracy.
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Datastat.ML
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstra…
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
LSTM models outperform traditional ARIMA in S&P 500 forecasting.
problem Forecasting volatile financial data with non-linear dependencies.
method Compared LSTM and ARIMA models using historical data and technical indicators.
result LSTM models outperformed ARIMA in accuracy and error metrics.
CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimizationcs.LG
Paper proposes CNN-LSTM model for multi-speaker speech separation.
problem Multi-speaker source separation using deep learning.
method Parallel CNN-LSTM architecture with Bayesian hyperparameter optimization.
result Parallel CNN-LSTM model outperforms LSTM-only and CNN-only models.
The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm…
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks, state of the art models in speech recogniti…
Deep learning LSTM predicts stock prices for portfolio design in Indian sectors.
problem Predicting stock prices in Indian stock market.
method Long Short-Term Memory (LSTM) model for historical stock price prediction.
result Efficacy of LSTM model in predicting stock prices and informing investment decisions.
Hybrid LSTM-GNN model improves stock price prediction accuracy.
problem Enhancing stock price prediction accuracy using complex data.
method Combining LSTM for time series and GNN for graph-based analysis.
result Hybrid model reduces MSE by 10.6% compared to standalone LSTM.
Stacked LSTM networks improve traffic volume forecasting.
problem Accurate traffic volume prediction for better planning.
method Applying stacked Long Short-Term Memory (LSTM) networks for time series forecasting.
result Stacked LSTM networks enhance the accuracy of traffic volume predictions.
Paper presents LSTM models for short-term stock price prediction.
problem Accurately predicting short-term stock prices is challenging.
method Univariate and multivariate LSTM models using historical data.
result Multivariate LSTM model with technical indicators outperforms univariate model.
Stability of the Decoupled Extended Kalman Filter Learning Algorithm in LSTM-Based Online Learningcs.LG
DEKF maintains stability in LSTM learning with bounded perturbations.
problem Stability of DEKF in LSTM-based online learning.
method Modeling DEKF as a perturbed extended Kalman filter and deriving stability conditions.
result DEKF learns LSTM parameters with similar stability properties to the global extended Kalman filter.
LSTM model predicts climate impacts on floods and droughts.
problem Predicting climate impacts on individual watersheds is challenging.
method Large-scale LSTM training on extensive data sets.
result LSTM model outperforms state-of-the-art models in predicting extreme flows.
DistPre predicts traffic speeds efficiently for large networks.
problem Fine-grained, accurate speed prediction for large-scale transportation networks.
method Customizes LSTM models on a cluster, sharing trained models between detectors.
result Efficient and accurate fine-grained traffic-speed prediction.
This paper optimizes portfolios of thematic sector stocks using LSTM models.
problem Designing an optimized portfolio of stocks to maximize return and minimize risk.
method Extracted stock prices from Jan 2016 to Dec 2020, used LSTM model for prediction, designed portfolios based on critical stocks.
result LSTM model accurately predicted future stock returns, indicating high accuracy.
Hybrid model combines LSTM and ETS for mid-term electric load forecasting.
problem Mid-term electric load forecasting accuracy.
method Combines LSTM, ETS, and ensemble learning; uses dilated LSTM for long-term relationships.
result High performance and competitiveness compared to classical and machine learning models.
Unified statistical framework for LSTM model selection.
problem Model selection and hyperparameter tuning in LSTM networks is heuristic and computationally expensive.
method Proposes a statistical framework extending classical model selection ideas to LSTM networks.
result Improved performance of the proposed framework demonstrated on biomedical data.
Study compares LSTM models with sentiment analysis for stock price prediction.
problem Efficient stock price prediction models using LSTM with sentiment analysis.
method Various types of LSTM models combined with sentiment analysis.
result Identifies the most effective model for short and long-term stock price prediction.