Time Series Prediction Based on LSTM-Attention-LSTM Model

Multivariate LSTM-FCNs for Time Series Classification

Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance. Our proposed models outperform most of the state of the art models while requiring minimum preprocessing. The proposed model...

Applying LSTM to Time Series Predi

Future Context Attention for Unidirectional LSTM Based Acoustic Model

Recently, feedforward sequential memory networks (FSMN) has shown strong ability to model past and future long-term dependency in speech signals without using recurrent feedback, and has achieved better performance than BLSTM in acoustic modeling. However, the encoding coefficients in FSMN is context-independent while context-dependent weights are commonly supposed to be more reasonable in acou...

Hashtag Recommendation with Topical Attention-Based LSTM

Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn...

On stock return prediction with LSTM networks

Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 5...