Evaluating the performance of different Long Short-Term Memory networks (LSTM’s) on financial timeseries data using mean squared error in order to identify the optimum LSTM variant for regression performance on financial timeseries data.
- - =#n/a
- - Academic And Stem
- - Academic Research: Pedagogy, Learning And Education
- - Biological Sciences: Biochemistry, Genetics, And Biotechnology
- - Botany, Plant Science And Agriculture
- - Chemistry
- - Civil Engineering
- - Commerce, Management And Economics
- - Computer Science & Information Technology
- - Electronics And Communication & Electrical Engineering
- - Environmental Science
- - Food Science, Health And Nutrition
- - Geography, Geology And Earth Science
- - History And Archaeology
- - Journalism And Mass Communication
- - Language, Literature And Linguistics
- - Law, Criminology And Forensics
- - Mathematics And Statistics
- - Mechanical Engineering
- - Medical Science
- - Pharmaceutical Sciences
- - Philosophy
- - Physics
- - Political Science
- - Psychology
- - Sociology
Book Details :
Evaluating the performance of different Long Short-Term Memory networks (LSTM’s) on financial timeseries data using mean squared error in order to identify the optimum LSTM variant for regression performance on financial timeseries data.