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Power Market Price Forecasting via Deep Learning
A study on power market price forecasting by deep learning is presented....
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A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting
A new variational mode decomposition (VMD) based deep learning approach ...
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Forecasting Commodity Prices Using Long Short-Term Memory Neural Networks
This paper applies a recurrent neural network (RNN) method to forecast c...
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Deep Learning for Energy Markets
Deep Learning (DL) provides a methodology to predict extreme loads obser...
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Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series
L'analyse prédictive permet d'estimer les tendances des évènements futur...
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Curriculum Learning in Deep Neural Networks for Financial Forecasting
For any financial organization, computing accurate quarterly forecasts f...
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Modeling and Probababilistic Forecasting of Natural Gas Prices
In this paper, we examine the problem of modeling and forecasting Europe...
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Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters
Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of SantaBarbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts.Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5 Deep Learning (DL) ensemble model found in literature by 33 yields and 21
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