  For the last few years it has been observed that the Deep Neural Networks
(DNNs) has achieved an excellent success in image classification, speech
recognition. But DNNs are suffer great deal of challenges for time series
forecasting because most of the time series data are nonlinear in nature and
highly dynamic in behaviour. The time series forecasting has a great impact on
our socio-economic environment. Hence, to deal with these challenges its need
to be redefined the DNN model and keeping this in mind, data pre-processing,
network architecture and network parameters are need to be consider before
feeding the data into DNN models. Data normalization is the basic data
pre-processing technique form which learning is to be done. The effectiveness
of time series forecasting is heavily depend on the data normalization
technique. In this paper, different normalization methods are used on time
series data before feeding the data into the DNN model and we try to find out
the impact of each normalization technique on DNN to forecast the time series.
Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing
index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by
using BSE and NYSE time series data.
