DECODING MARKET TRENDS WITH LONG SHORT-TERM MEMORY

Abstract

Stock trading is one of the most common business activities, playing a crucial role in financial endeavors. This abstract stock prediction involves utilizing various techniques, ranging from market research to tracking market share values and trends. The learning curve for understanding share price trend forecasting is sharp and time-consuming for ordinary citizens. However, with advancements in technology, machine learning algorithms better human capabilities in accuracy. Market swings occur due to the difficulty in predicting stock market trends accurately. Computational capacity is now widely available, enabling the development of machine learning algorithms using Python. These algorithms leverage existing stock market data to make accurate predictions. This article presents a model developed using LSTM, a recurrent neural network architecture proven to be highly effective in imitating human cognition. Deep learning techniques are employed using a large dataset to make efficient analyses and forecasts regarding market trends. The dataset used in this work comprises price history data from the year 2010 downloaded from Yahoo Finance. To address accuracy issues and enhance visual data presentation, different optimizers such as Adam and RMSprop are utilized in the experimentation process. Additionally, univariate and multivariate analyses are incorporated into the project task to further enhance accuracy.

Specification

Description:The present invention provides a novel method and system for accurately predicting stock market trends and share prices using advanced machine learning techniques. This invention specifically leverages Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN) architecture, to model the sequential nature of stock price data and effectively capture long-term dependencies.
• The invention is designed to improve prediction accuracy by incorporating various deep learning techniques and optimization algorithms such as Adam and RMSprop. These optimizers enhance the training process, enabling faster convergence and greater precision in predicting future stock movements.
• The model is trained on a large dataset of historical stock prices, such as those available from Yahoo Finance, spanning over a decade of market activity. Both univariate and multivariate analyses are supported, allowing the model to predict stock prices based on a single variable (e.g., past stock prices) or multiple variables (e.g., other financial indicators), thus improving its adaptability and robustness in various market conditions.
• This invention addresses the steep learning curve typically associated with financial forecasting by offering an automated, user-friendly solution. Ordinary investors, as well as financial institutions, can benefit from the increased accuracy, efficiency, and accessibility provided by this LSTM-based stock prediction model.

, Claims:1. A method for predicting stock prices using a Long Short-Term Memory (LSTM) neural network, comprising:
a. Collecting and preprocessing historical stock data;
b. Training the LSTM model with the preprocessed data;
c. Applying optimization algorithms, including Adam and RMSprop, to improve accuracy;
d. Generating and outputting stock price predictions.
2. The method of claim 1, wherein the prediction is based on univariate analysis of past stock prices.
3. The method of claim 1, wherein the prediction is based on multivariate analysis using additional financial indicators.
4. A system for stock price prediction, comprising:
a. A data collection module for historical stock data;
b. An LSTM neural network for trend analysis;
c. An optimization module using Adam and RMSprop;
d. An output module for predicted prices.
5. The system of claim 4, further configured for continuous learning and updates with new stock data.

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