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Forecasting

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Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting.

IEEE journal of biomedical and health informatics
Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models hav...

Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection.

PloS one
With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, ...

Design of a Regional Economic Forecasting Model Using Optimal Nonlinear Support Vector Machines.

Computational intelligence and neuroscience
Forecasting regional economic activity is a progressively significant element of regional economic research. Regional economic prediction can directly assist local, national, and subnational policymakers. Regional economic activity forecast can be em...

A novel combined model for prediction of daily precipitation data using instantaneous frequency feature and bidirectional long short time memory networks.

Environmental science and pollution research international
Meteorological events constantly affect human life, especially the occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent...

The Design of Adolescents' Physical Health Prediction System Based on Deep Reinforcement Learning.

Computational intelligence and neuroscience
According to the general recognition in the first half of the last century, hypertension was not considered a kind of disease, but was regarded as a compensatory response commonly seen in the elderly, and it would not occur to younger people. Because...

Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks.

Sensors (Basel, Switzerland)
As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows mo...

Deep learning via LSTM models for COVID-19 infection forecasting in India.

PloS one
The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lac...

LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index.

Sensors (Basel, Switzerland)
We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of ...

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Computational intelligence and neuroscience
Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehen...