1) Keras and tensorflow with the following libraries.
   - numpy, pandas, scikit-learn, scipy, matplotlib, statsmodels
2) It is enough to download files at the same directory and run the python files.


About:
Many of the deep learning solutions for time-series forecasting reported in the literature
include complex neural networks that may not be directly employed by the practitioner in the field. In this
study, we demonstrate how the standard deep neural network types convolutional neural networks (CNN)
and long short-term memory (LSTM) network can be applied in the field of time-series forecasting. CNN
and LSTM models outperform traditional methods when their performances are evaluated on the univariate
electric consumption data of Illinois, USA between 2005 and 2011.We also illustrate the use of hybrid deep
learning architectures composed of standard CNN and LSTM for data fusion with the aim of time-series
forecasting. When hybrid architectures are applied to the fused data of the electric consumption data and the
multivariate weather data of Illinois, USA between 2005 and 2011, the forecasting performance is improved
compared to that when only univariate data is used. Furthermore, we employ these models on a small size
dataset to apply transfer learning and show that they perform well in the case of lack of data.