An Empirical Study of Time Series Forecasting Using Boosting Technique with Correlation Coefficient

2007 ◽  
Author(s):  
Luzia Vidal de Souza ◽  
Aurora T. R. Pozo ◽  
Anselmo Chaves Neto
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Correlation Coefficient ◽  
Time Series Forecasting ◽  
Boosting Technique

scholarly journals Genetic Programming and Boosting Technique to Improve Time Series Forecasting

10.5772/9617 ◽  
2009 ◽  
Author(s):  
Luzia Vidal de Souza ◽  
Aurora T. R. Pozo ◽  
Anselmo C. ◽  
Joel M. C. da Ros
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Time Series Forecasting ◽  
Boosting Technique

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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