Meteorological data mining for climate precipitation prediction using neural networks

Anochi, Juliana Aparecida; Velho, Haroldo Campos

doi:10.6062/jcis.2015.06.02.0096

Cited by 3 publications

(1 citation statement)

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“…Structure of MLP with backpropagation algorithm (Anochi & Campos Velho, 2016) where N is the number of patterns (one for each inputstarget pair) used in the training process, t kp and o kp are the target value and the output obtained at output node k, respectively.…”

Section: The Multilayer Perceptronmentioning

confidence: 99%

High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model

2022

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Risk permeates more and more financial markets around the world. It is an essential element that all financial market actors attempt to model and manage. This paper proposes a novel model that improves the predictive accuracy of high frequency volatility forecasts. The ANN‐MC‐GARCH model is therefore developed in this research. The hybrid model enhances the MC‐GARCH conditional variance model by including endogenous market variables in a feed forward network which models volatility in terms of past disturbances and variances. The forecasting accuracy of the novel ANN‐MC‐GARCH is evaluated against the classical MC‐GARCH model. The empirical investigation employs the 1‐min high frequency observations of four exchange rates (USD/EUR, USD/GBP, USD/JPY & AUD/JPY), three market indices (France 40, UK 100 & USA 500) and two metal commodity indices (Spot Gold & Spot Silver). The backtesting method employs the RMSE and MAE performance metrics, the out‐of‐sample , the Diebold‐Mariano test and the Model Confidence Set (MCS) procedure. The empirical findings show that the hybrid model is superior to the MC‐GARCH model for the nine datasets.

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Section: The Multilayer Perceptronmentioning

confidence: 99%

High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model

2022

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Forecast of Convective Events Via Hybrid Model: Wrf and Machine Learning Algorithms

da Silva,

França,

Ruivo

et al. 2022

SSRN Journal

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Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

Arifoğlu

Kandemir

2022

Mehmet Akif Ersoy Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.

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Meteorological data mining for climate precipitation prediction using neural networks

Cited by 3 publications

References 0 publications

High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model

High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model

Forecast of Convective Events Via Hybrid Model: Wrf and Machine Learning Algorithms

Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

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