A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
QLSTM outperforms LSTM in predicting KSE 100 index movements.
Much sequential data exhibits highly non-uniform information distribution. This cannot be correctly modeled by traditional Long Short-Term Memory (LSTM). To address that, recent works have extended LSTM by adding more activations between adjacent inputs. However, the approaches often use a fixed depth, which is at the …
Stacked LSTM networks improve traffic volume forecasting.
Extended LSTMs improve volatility prediction by 20%.
Simplified LSTM models improve sentiment analysis on Twitter debate data.
LSTMs improve demand forecasting for e-grocery products.
Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems. In particular, long-short term memory (LSTM) recurrent neural networks have achieved state-of-the-art results in many speech recognition tasks, due to their efficient representation of long and short term dependencies …
Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our …
LSTMs improve bond yield forecasting with unique signals.
In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into var…
The paper uses HMM and LSTM for stock market trend analysis.
New LSTM model predicts stock market prices with improved accuracy.
Extended LSTM improves language modeling performance.
Paper presents methods to create stock price confidence intervals using LSTM models.
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothin…
NeuroMemFPP uses LSTM to estimate FPP parameters with high accuracy.
LSTM neural networks improve fiber nonlinearities in coherent systems.
Paper questions RNN and LSTM's long-term memory and introduces a new definition.
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we conside…
LSTM models outperform traditional ARIMA in S&P 500 forecasting.
With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box. In this paper it is analyzed how recurrent neural network (RNNs) cope with temporal dependen…
Paper proposes new method for time series confidence intervals using LSTM.
Hybrid model forecasts Bitcoin prices better than standard LSTM.
Deep learning predicts currency volatility accurately.
LSTM predicts COVID-19 growth patterns from global data.
New RNN model handles long-term dependencies in irregularly-sampled time series.
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
Hybrid model improves traffic flow prediction accuracy.
SRNNs achieve LSTMs' online regression performance in less time.
Paper uses LSTM neural networks to forecast commodity prices.
State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many appli…
LSTM-MDNs improve risk forecasting during turbulent periods.
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully u…
EnLSTM network improves log generation from small datasets.
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet con…
Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient probl…
LSTMs outperform DFM in nowcasting COVID-19 economic variables.
Automated detection of voice disorders with computational methods is a recent research area in the medical domain since it requires a rigorous endoscopy for the accurate diagnosis. Efficient screening methods are required for the diagnosis of voice disorders so as to provide timely medical facilities in minimal resourc…
Optimizes investment model using LSTM for better risk control.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a…
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
Paper addresses LSTM stability for thermal systems using infinity-norm.
Deep learning models improve stock portfolio performance.
Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells. However, the extra parameters associated with the memory cells mean an LSTM layer has four times as many parameters as an RNN with the…
In this work, we first analyze the memory behavior in three recurrent neural networks (RNN) cells; namely, the simple RNN (SRN), the long short-term memory (LSTM) and the gated recurrent unit (GRU), where the memory is defined as a function that maps previous elements in a sequence to the current output. Our study show…
Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to em…
Event-driven features improve forex price prediction accuracy.