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Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization · Study

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ArticleForecasting Financial Time Series Using Robust Deep Adaptive Input Normalization

Nikolaos Passalis2021

Article2021

Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization

Nikolaos Passalis · Juho Kanniainen · Moncef Gabbouj · Alexandros Iosifidis · Anastasios Tefas

24 citations·0/0 files stored·Journal of Signal Processing Systems

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

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References

1960A Coefficient of Agreement for Nominal Scales 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2016Deep Direct Reinforcement Learning for Financial Signal Representation and Trading 2018Forecasting Economics and Financial Time Series: ARIMA vs. LSTM 2018Forecasting stock market crisis events using deep and statistical machine learning techniques 2018Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis 2017Using deep learning to detect price change indications in financial markets 2020Using Deep Learning for price prediction by exploiting stationary limit order book features

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Year2021

Typearticle

Published2021-01-22

VenueJournal of Signal Processing Systems

ISSN-L1939-8018

Languageen

Citations24

Files0 public / 0

ShelfStock Market Forecasting Methods

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doi10.1007/s11265-020-01624-0

openalex_workW3122765894

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