A STUDY ON: FORECASTING MARKET TRENDS USING TIME SERIES ANALYSIS AND MACHINE LEARNING
1 ) Identify two research problems relevant to your discipline and justify the significance of the study.
Many challenges arise when forecasting market trends using time series analysis and machine learning. Here are some of the most problematic issues we encounter in this process, like external events, uncertainty, data limitations, etc. Which can complicate the modeling of our models using machine learning and time series analysis
Real-time Analysis:
PROBLEM :
In many applications, such as stock market prediction, real-time analysis is essential.
Developing models that can process and analyze data in real-time while maintaining accuracy is a challenging task.
SOLUTION :
Implement streaming data processing to handle real-time data updates.
Use algorithms that can update models dynamically as new data arrives.
Employ techniques like rolling forecasts or sliding window approaches for continuous prediction updates.
Data Quality and Missing Values:
PROBLEM :
Time series data often suffer from missing values or inconsistencies, which can arise due to various reasons such as data collection errors, sensor failures, or gaps in reporting.
SOLUTION :
Clean and preprocess data to handle missing values appropriately.
Impute missing values using methods like mean, median, or sophisticated techniques such as interpolation.
Ensure data consistency and accuracy to avoid biased forecasts.
Significance of Forecasting market trends :
Forecasting market trends means predicting what people will want to buy in the future.
It's important because it helps businesses make plans.
For example, if a company sees that more people are interested in buying eco-friendly products, they might start making more of those.
It also helps investors decide where to put their money so they can make more profit.
Governments use it too, to make rules that help the economy grow.
Overall, forecasting market trends helps everyone make better decisions about what to sell, buy, or invest in.
Helps Businesses Plan: It lets businesses know if more people will want certain products soon. This helps them decide how much to make, how to sell it, and where to sell it.
Helps Investors Make Money: Investors use trend forecasts to decide which companies to invest in. For example, if they think a certain product will become less popular, they might invest their money somewhere else to stay safe.
Reduces Risks: By knowing what's going to be popular, businesses can avoid making too much of something that won't sell well. They can also spot opportunities to make new products that people will like.
Gives Businesses an Edge: Companies that know what people will want in the future can make those products first. This makes them more popular and helps them make more money.
Uses Resources Wisely: Knowing what people will buy helps companies use their workers, materials, and money in the best way possible.
Better Marketing: When companies know what's popular, they can advertise and sell their products better. This makes more people want to buy from them.
Helps Governments Make Rules: Governments use trend forecasts to make rules that help businesses grow and keep the economy strong.
Understands Customers: Figuring out what people like helps businesses make products that customers will love. This makes customers happy and helps businesses grow.
3.RESEARCH DESIGN
1. Research topic
→ Forecasting Market Trends Using Time Series Analysis and Machine Learning
2. Objectives of the studies
To analyze historical market data to identify trends and patterns.
To develop and compare predictive models using time series analysis and machine learning techniques.
3. Universe(area of study)
The study will focus on the financial markets, specifically stock prices of selected companies within the technology sector in the United States.
4. Sample size - 100
5. Types of data - Primary data
6. Methods of primary data collection
Surveys/Interviews: Conducting surveys or interviews with financial analysts to gather insights on market sentiment.
Data sets from kaggle ( if it is secondary data collection )
7. Methods of sampling
Probability Sampling
Non-Probability Sampling
8. Tool of data collection
APIs: Financial APIs for data retrieval.
Survey Platforms: Google Forms or SurveyMonkey for collecting qualitative data.
Data sets platform : kaggle, Google cloud etc.
Structured and unstructured.
9. Method of data analysis
Descriptive Statistics
Time Series Analysis
Machine Learning Models
10. Budget of research ( estimated cost ) - ₹50,000
11. Date of submission - December 24, 2024
4. PROPOSAL ON AN IDENTIFED RESEARCH PROBLEM
1. Research Topic :
Forecasting Market Trends Using Time Series Analysis and Machine Learning
2.Significance of the Research topic :
Business Planning: Helps in anticipating demand and optimizing supply chains.
Investment Decisions: Guides profitable investments by identifying emerging trends.
Risk Reduction: Avoids overproduction and capitalizes on new opportunities.
Competitive Advantage: Enables companies to be first to market with new products
3.Objectives :
1. Develop real-time analysis models for accurate forecasting.
2. Enhance data quality by addressing missing values and inconsistencies.
3. Evaluate model performance for improved forecast accuracy.
4. Provide practical guidelines for implementation
4. Research Methodology ( Research Design ) :
Research Design
Real-Time Data Processing: Utilize streaming data frameworks and dynamic machine learning models to handle real-time updates.
Data Quality Improvement: Implement advanced data cleaning and imputation techniques.
Model Evaluation: Use historical data to benchmark models, evaluating accuracy with metrics like MAE and RMSE.
Data Collection
Gather historical and real-time market data from diverse sources to ensure model robustness across sectors.
5) Review of literature :
Time Series Analysis and Forecasting:
Box, G.E.P., Jenkins, G.M., & Reinsel, G.C. (2015). Time Series Analysis: Forecasting and Control.
Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice.
Machine Learning for Time Series Forecasting:
Brownlee, J. (2018). Deep Learning for Time Series Forecasting.
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions.
6) Chapterization :
Chapter 1 : Introduction
Chapter 2 : Review of literature
Chapter 3 : Research Methodology
Chapter 4 : Analysis of Objective 1
Chapter 5 : Analysis of Objective 2
Chapter 6 : Finding of the Research
Chapter 7 : Conclusion , Recommendation and Bibliography
7) Budget : ₹ 50,000
5. REVIEW OF AN ARTICLE
Pradeep (2023) conducted research on generative strategies for enhancing computer vision training with synthetic data. This article shows how we can use synthetic data, i.e., artificially generated data, as a substitute for real-world data in various applications due to limited labeled datasets. The article aims to enhance the efficacy of computer vision models through the incorporation of synthetic data.
6. VARIABLE OF RESEARCH STUDY
Variables from this article:
Synthetic data (artifically generated) and real-world data
Efficacy of computer vision
7. VISIT COLLEGE LIBRARY AND SELECT 5 RESERCH TITLES OF
RESEARCH PURPOSE
Author : Ali Asgarov
Research Title: Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing
Author : Deepak Doreswamy and Subraya Krishna Bhat
Research Title: Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models
Authors: Li, Y., and Pan, Y.
Research Title : A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
Author : Mine Konur, M. Göçken, A. T. Dosdoğru
Research Title : Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators
Author : M. Aiken, Mohammad Z. Bsat
Research Title: :Forecasting Market Trends with Neural Networks