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With the evolution of distributed streaming platforms analysing humongous time series data, which is streamed continuously from IoT devices become lot easier. In most of the IoT networks the data are in motion or in data centre/cloud. It is possible to process this data in real time similar to edge devices using the big data framework.  In data intensive applications predictive analytics require more resources to perform complex computations. Apache Flink framework is capable of performing real time streaming of schema less data and scales very high in distributed environment with low latency, it is used to collect and store the data in the cloud. This work suggests a suitable environment to collect, transport, preprocess and aggregate the data stream to perform predictive analytics using deep learning models. Deep learning automatically extracts features and builds models after training, it has the potential to solve problems that can't be solved by conventional machine learning models. Therefore, the use of algorithms based on deep learning is recommended for forecasting temporal data. Also, we discuss a number of different deep learning forecasting models and analyse the performance of different deep learning forecasting models in order to determine which one is the effective model for single step, multi step and multi variant methods based on error functions with respect to streamed sensor data.

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