Applying Deep Learning to Time Series Forecasting with Python

Applying Deep Learning to Time Series Forecasting with Python

In this project, we will use traditional time series forecasting methods as well as modern deep learning methods for time series forecasting.

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SUBHABRATA BISWAS

Lead Consultant, ITC Infotech

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

Swati Patra

Systems Advisor , IBM

I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More

What will you learn

AR Models, MA Models
ARIMA Models
NN Models
DNN Models
RNN Models
LSTM Models

Project Description

The term "univariate time series" refers to a time series that consists of single (scalar) observations recorded sequentially over equal time increments.A common assumption in many time series techniques is that the data are stationary.​ A stationary process has the property that the mean, variance and autocorrelation structure do not change over time.​ We will use traditional time series forecasting methods as well as modern deep learning methods for time series forecasting. ​​

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Curriculum For This Mini Project