Predicting Support and Resistance Indicators for Stock Market with Fibonacci Sequence in Long Short-Term Memory
- 1 D.G. Vaishnav College, Chennai, India
- 2 Chennai-600106, Tamil Nadu India, India
Predictive data analytics is a branch of data analytics where models are designed that effectively interpret; anticipate outcomes by analyzing present data to make predictions about future. One of the major attributes for prediction is time and there have been a considerable number of time series analytical models that are used for forecasting. Long Short-Term Memory (LSTM) is a deep learning model which is used as a time series model and this research work had made an attempt to apply Fibonacci sequence for retracement of the support and resistance levels, one of the commonly used trend indicators and used LSTM for predicting those levels. These levels help identify the uptrend or downtrend that decide the buying or holding of shares. For this purpose, datasets from financial sector was taken and split into 80% of training data and 20% of testing data for the analysis. The source of these datasets is Kaggle and each of these dataset has a total of 5021 instances from January 2000 till February 2020. Scaling of data was done, retracement of values using three Fibonacci percentages along with the starting and ending level was applied using the LSTM network. For measuring the accuracy of the model, the Root-Mean-Square Error (RSME) metric was used. The comparison between the error score with the lowest value of the dependent variable determines the level of accuracy which is expected to be less in LSTM model that is to be conformed as a test case.
Copyright: © 2020 Thambusamy Velmurugan and Thiruvalluvan Indhumathy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Support Value
- Resistance Value
- Fibonacci Retracement
- Long Short-Term Memory
- Root-Mean-Square Error Metric