sai-teja-ponugoti / Stock_Price_Prediction

Using time series data to predict the furture stock price using previous data

Stock Price Prediction

Using time series data to predict the furture stock price using previous data

Overview

• Time Series is a collection of indexed data points based on the time during which they were collected. The data is most often recorded at regular time intervals.

• In practise, predicting future values for the time series is a very common problem. Predicting next week's weather, stock prices, tomorrow's Bitcoins price, the amount of your Chrismas sales and potential heart disease are common examples of this.

• Recurrent neural networks ( RNNs) may predict, or classify, the next value(s) in a series. A series is stored as a matrix, where each row is a descriptive vector of a function. The order of the rows in the matrix is of course essential.

• Time Series is just one type of a sequence. We’ll have to cut the Time Series into smaller sequences, so our RNN models can use them for training.

• Classic RNNs have memory issues (long-term dependencies). The beginning of the sequences that we use for training appears to be "forgotten" due to the overwhelming effect of more recent states.

• In general, these problems can be overcome by using gated RNNs. They can store information,just like having a memory, for later use. The data learns to read, write, and erase from the memory.

• The two most commonly used gated RNNs are Long Short-Term Memory Networks and Gated Recurrent Unit Neural Networks.We will try both these RNNs for or application and select one from it.

Refer to stock_price_documentation.ipynb for further details on the implementation.

Output Samples:

• When 3 previous days prices as considered as features

• When 5 previous days prices as considered as features

If used give credits by forking, staring or watching git hub repo or in some other way.:slightly_smiling_face: