QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
problem Existing prediction methods often ignore the distinction between long-term trends and short-term fluctuations.
method The paper introduces a MTS forecasting framework that uses both original time series and its first difference to capture long-term trends and short-term fluctuations.
result The proposed method improves forecasting performance by using more supervision information.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term 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.
Deep learning predicts baseball home runs with better accuracy.
problem Predicting player performance in baseball.
method Long Short-Term Memory (LSTM) neural network.
result LSTM outperformed other models in predicting home runs.
Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …
We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and e…
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
Comparative study of neural networks for short-term FOREX forecasting.
problem Simulating expert judgment in foreign exchange market forecasting.
method Implemented and compared LSTM and ANN architectures for short-term FOREX forecasting.
result ANN custom architecture outperforms LSTM in prediction quality and resource efficiency.
Paper proposes a method for predicting any quantile of short-term electricity demand.
problem Uncertainty in power systems due to multiple factors.
method Proposes a novel general approach for distributional forecasting of short-term electricity demand.
result Demonstrates state-of-the-art distributional forecasting results for short-term electricity demand.
The paper predicts travel times using tree-based ensembles.
problem Predicting travel times between urban points over short and long horizons.
method Tree-based ensemble methods trained on taxi trip records with additional features from weather and routing data.
result Adding routing data improves model performance and short-term predictions require less data.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
problem Short-term power prediction in commercial buildings with high accuracy.
method Unsupervised extraction of device profiles from aggregate power measurements, disaggregation using particle swarm optimization, and state changes forecast by artificial neural networks.
result Developed approach outperforms existing methods with high accuracy.
Predicts S&P 500 trends using machine learning models.
problem Market trend prediction for S&P 500 index.
method Feature engineering, machine learning models (Logistic Regression, Decision Trees, Random Forests, Neural Networks, KNN, XGBoost), data preprocessing, hyperparameter tuning, SMOTE.
result KNN for short-term predictions, XGBoost for long-term forecasts.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
New approach models computer network activity as mixtures of sources.
problem Malicious activity detection in computer networks using standard algorithms is ineffective.
method Source separation approach to model short-term dynamics of computer network activity.
result Qualitative and quantitative experiments validate the approach.
Deep learning predicts cryptocurrency price movements from trade data.
problem Predicting short-term price changes in cryptocurrencies.
method Long Short-term Memory Network (LSTM) trained on trade-by-trade data.
result Optimal LSTM model achieves over 60% accuracy on out-of-sample test periods.
This study compares machine learning models for short-term stock price forecasting.
problem Accurate short-term stock price prediction in the NYSE.
method Compared four machine learning models (XGBoost, Random Forest, Multi-layer Perceptron, Support Vector Regression) on NYSE stocks.
result XGBoost model outperformed others with highest accuracy.
The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this …
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
Deep learning predicts currency volatility accurately.
problem Predicting future volatility in Forex trading.
method Constructed a deep-learning network using multiscale LSTM with multi-currency pairs.
result Multiscale LSTM model outperforms conventional models.
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…
Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method uti…
The paper proposes a method to identify high-quality financial patterns using entropy.
problem Extracting reliable short-term patterns from noisy financial data.
method Entropy-assisted framework for clustering and pruning patterns.
result High-quality patterns with low local entropy and historical profitability.
Event-driven features improve forex price prediction accuracy.
problem Inaccurate predictions in forex due to market volatility.
method Developed event-driven features and used LSTM, BiLSTM, GRU models.
result Improved prediction system with minimal risk.
LSTM predicts COVID-19 growth patterns from global data.
problem Forecasting the spread of COVID-19 using big data and machine learning.
method Used multivariate long short-term memory (LSTM) to learn correlations over time.
result LSTM outperformed RNN in predicting COVID-19 growth with lower validation error.
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
problem Short-term weather predictions for adverse events like heavy rain and flooding.
method Analysis of existing radar-based nowcasting techniques and machine learning approaches.
result New partnerships between environmental science and machine learning can improve nowcasting.
With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to i…
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
problem Uncertainty in crypto coin values.
method Long Short Term Memory, Support Vector Machine, Polynomial Regression models.
result Support Vector Machine with linear kernel had the smallest mean square error.
Framework improves PV forecasting by accounting for missing data uncertainty.
problem Uncertainty from missing data in PV power data.
method Combines stochastic multiple imputation with Rubin's rule.
result Improves prediction interval calibration without sacrificing point prediction accuracy.
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.
Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price fo…
This study uses LSTM and SARIMA models to forecast CPU usage in cloud computing.
problem Predicting future CPU usage in cloud computing centers.
method Used LSTM and SARIMA models to forecast CPU usage over short and long time scales.
result SARIMA model performed better for long-term predictions but LSTM was more robust.
Study uses neural networks to predict travel times for public transportation.
problem Inaccurate travel time predictions due to road traffic irregularities.
method Developed two neural network models (MLP and LSTM) using OD travel time matrix.
result Both models can make near-accurate predictions, but LSTM is more susceptible to noise.
Adaptive TFTs improve cryptocurrency price prediction accuracy.
problem Precise short-term price prediction in volatile cryptocurrency markets.
method Dynamic subseries lengths and pattern-based categorization.
result Significantly outperforms baseline models in prediction accuracy and profitability.
Paper uses AI methods to forecast Bitcoin prices.
problem Inaccurate Bitcoin price predictions in previous studies.
method Combines EEMD and LSTM for next-day price forecast.
result Improves Bitcoin price prediction accuracy.
Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires man…
Extended LSTMs improve volatility prediction by 20%.
problem Predicting asset price volatility with long memory.
method Extended LSTMs with multiple flexible timescales.
result Extended LSTMs outperform rough volatility predictions by 20%.
Study enhances neural network predictions for wave height using topological features.
problem Challenges in predicting wave heights due to short-term and long-term factors.
method Hybridization of persistent homology with neural networks for feature engineering.
result Significant improvements in R2 score and reductions in errors for various neural network models. In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural n…
The paper calibrates geophysical predictions using marginal distributions and machine learning.
problem Sensitivity to initial conditions in geophysical systems leads to large deviations in long-term forecasts.
method The method introduces a calibration algorithm based on normalization and Kernelized Stein Discrepancy (KSD) to enhance ML predictions.
result The method improves the fidelity of ML predictions to known physical distributions, ensuring consistency with non-local statistical structures.
Paper proposes a new time series prediction method using weighted past data and optimization.
problem Predicting time series data with improved accuracy considering both deterministic and stochastic assumptions.
method The approach uses a weighted sum of past data, solving a constrained linear optimization problem to minimize an outer bound of prediction error.
result The method can outperform existing non-parametric methods in short-term forecasts.
Study proposes a multimodal model for cardiovascular risk prediction using EHRs.
problem Lack of comprehensive risk prediction from EHRs due to unstructured text.
method Proposes a multimodal BiLSTM model integrating structured and unstructured EHR data.
result Proposed BiLSTM model outperforms other DNN architectures in cardiovascular risk prediction.
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can b…
The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for predicting return jump arrivals in equity markets with high-frequency limit order book data. This new architecture, based on …
Model predicts short-term Amazon rainforest fires with high accuracy.
problem Accurate short-term forecasting of Amazon rainforest fires is challenging.
method Used Seasonal and Trend decomposition based on Loess combined with multi-month-ahead load forecasting algorithms.
result Proposed decomposition-ensemble models provide more accurate forecasts than other models.
condLSTM-Q predicts COVID-19 deaths at county level with quantile forecasts.
problem Predicting COVID-19 mortality at fine geographical scales.
method Conditional Long Short-Term Memory networks with quantile output.
result Fine-scale quantile predictions inform about death toll distribution.