Forecasting Corporate Financial Time Series using Multi-phase Attention Recurrent Neural Networks

When applying PETRA to the case of stock prices, we can capture the intrinsic dynamics of the stocks’ (and companies’) relationships, through the recognition of points in time

Typical traffic sections (time series) that are typical for various network user behavior models The following models of network user behavior are considered (based on

Our experiments comparing baseline approaches and classical time series forecasting methods to our method show that this assumption is fitted by most of the time series of our

Dans cet expos´ e, on cherche ` a r´ esoudre le probl` eme de pr´ ediction aveugle, c’est ` a dire lorsque la covariance est inconnue et que l’on doit, avec un unique ´

Data Augmentation for Time Series Classification using Convolutional Neural Networks.. Arthur Le Guennec, Simon Malinowski,

We compare the performance of our approach with the following four other prediction methods: mean season, Markov model (MM), autoregressive models (AR) and an algorithm

We observe that forecasting the centroid of the known cluster for day T + 1 (omniscient method) outperforms significantly the daily mean of the whole time series.. This means that

2.6 Summary statistics of the 1-step ahead forecast