# Future Stock Price Prediction Using Recurrent Neural Network, LSTM And .

6m ago
2 Views
623.58 KB
5 Pages
Last View : 1m ago
Transcription

Special Issue - 2021International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181ICRADL - 2021 Conference Proceedings3)Output gate — the input and the memory of the block isused to decide the output. Sigmoid function decides whichvalues to let through 0,1. and tanh function gives weightageto the values which are passed deciding their level ofimportance ranging from-1 to 1 and multiplied with outputofSigmoid.(ii) LSTM (LONG SHORT TERMMEMORY)Figure 2 Long Short Term MemoryLSTM’s have a Nature of Remembering information for along periods of time is their Default behavior. look at thebelow figure that says Every LSTM module will have 3 gatesnamed as Forget gate, Input gate, Output gate.Figure 6 Output gate3.DATA THAT IS USED IN THEMODEL:In the Model Two types of data are being used. They are(i) Train Data(ii) TestData(i) TrainData:In the model Train Data of 4 Years of Google Stock Prices isused. This data will be used to train our model. We useepochs of about 200 to get more accuracy.Figure 3 Long Short Term Memory Gates1) Input gate — discover which value from input should beused to modify the memory. Sigmoid function decides whichvalues to let through 0,1. andtanh function gives weightage tothe values which are passed deciding their level ofimportance ranging from-1 to1.(ii) TestData:In the Model Test data of 1 year of Google stock price isused to test our data. This data is used to test our modelforaccuracy.4.PROJECT IN PYTHONENVIRONMENT:Figure 4 Input gate2)Forget gate — discover what details to be discarded fromthe block. It is decided by the sigmoid function. it looks at theprevious state(ht-1) and the content input(Xt)andoutputs anumber between 0(omit this)and 1(keep this)for each numberin the cell state Ct 1.Figure 7 Project in python environmentFigure 5 Forget gateVolume 9, Issue 5Published by, www.ijert.org305

Special Issue - 2021International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181ICRADL - 2021 Conference ProceedingsFigure 8 Project GUI InterfaceIn the model the project in python environment the GUIInterface consists of three buttons (i) Open Training file,(ii)Open Test file, (iii) Click to get Graph of result of model,graph of predicted future 30 days and theclosevalueofpredicted 30 days. Each button is written under a functiondefinition in python environment. Where the first button isused to open the train data, the second media is used to opentest data, the third button is used to run the RNN and LSTMand fit the model to the RNN. And the code is also writtenfor the prediction of next future 30 days. There is also codefor plotting the graph of result of the model, graph forpredicted close prices for next future 30 days and there isalso code written for displaying the values of predicted 30days Close values.Figure 10 Initializing the RNNFigure 11 Prediction of stock prices for future 30 days.Figure 9 Functions for the buttonsFigure 12 Plotting the graphVolume 9, Issue 5Published by, www.ijert.org306

Special Issue - 2021International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181ICRADL - 2021 Conference ProceedingsFigure 13 Resultant graph5.RESULTS:Figure 16 Graph of Predicted Stock PriceFigure 14 Resultant graph of model of predictionFrom the above Graph we can see that the model haspredicted the stock prices more accurately. The model haspredicted the results more accurately. This graph comparesthe actual stock price with the predicted stock price and wecan see that the model has predicted the stock prices moreaccurately.Figure 17 The Close prices of predicted 30 days6. NOVELTY OF THEPROPOSED MODEL:Figure 15 Graph of Predicted Stock Price of future 30 dayVolume 9, Issue 5The quality of being new in my project is that in my StockPrice prediction model the prediction is more accurate thanother existing models and my project is also different in away that I had created a Graphical User Interface (GUI)where we can upload the Train data, Test Data and we canget the result of the model and the future 30 days predictedgraph with the stock prices. In my Project I had created togive a result graph which consists of the Future 30 daysPublished by, www.ijert.org307

prediction of stock prices will be more accurate. And also in the model it can predict the future 30 days Stock Prices and it can show it in a graph. Also [5] the main feature is that the model can show an output of the Individual Predicted Close prices of the Predicted 30 days as shown in the figurebelow. 9. CONCLUSION:

Related Documents:

Stock price prediction is regarded as one of most difficult task to accomplish in financial forecasting due to complex nature of stock market [1, 2, 3]. The desire of many . work are historical daily stock prices obtained from two countries stock exchanged. The data composed of four elements, namely: open price, low price, high price and

stock prices then an increase in the -rm s own stock price informativeness reduces the sensitivity of its investment to its peer stock price (prediction 1). Indeed, as the signal conveyed by its own . stock price (prediction 2), but not otherwise. The same prediction holds for an increase in the correlation of the fundamentals of a -rm .

The stock market is dynamic, non-stationary and complex in nature, the prediction of stock price index is a challenging task due to its chaotic and non linear nature. The prediction is a statement about the future and based on this prediction, investors can decide to invest or not to invest in the stock market [2]. Stock market may be

1. BASIC INTRODUCTION OF STOCK MARKET A stock market is a public market for trading of company stocks. Stock market prediction is the task to find the future price of a company stock. The price of a share depends on the number of people who want to buy or sell it. If there are more buyers, then prices will rise. If the seller has a number of .

the relationship between stock prices and these factors. Although these factors will temporarily change the stock price, in essence, these factors will be reﬂected in the stock price and will not change the long-term trend of the stock price. erefore, stock prices can be predicted simply with historical data.

An ecient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination Hakan Gunduz* Introduction Financial prediction, especially stock market prediction, has been one of the most attrac - tive topics for researchers and investors over the last decade. Stock market .

of a stock is known to be unpredictable[Walczak, 2001; Nguyenet al., 2015], research efforts have been focused on predicting the stock price movement e.g., whether the price will go up/down, or the price change will ex-ceed a threshold which is more achievable than stock price prediction[Adebiyi et al., 2014; Fenget al., 2018; Xu and Cohen, 2018].

Figure 3: Price prediction for the Apple stock 10 days in the future using Linear Regression. It is interesting how well linear regression can predict prices when it has an ideal training window, as would be the 90 day window as pictured above. Later we will compare the results of this with the other methods Figure 4: Price prediction for the .