Week 9: Time Series Analysis in Finance

Master the techniques of time series analysis for financial data, from basic trend analysis to advanced forecasting methods.

Learning Objectives:

✓ Understand the components of time series data (trend, seasonality, noise)
✓ Learn techniques for time series decomposition and analysis
✓ Apply moving averages and exponential smoothing
✓ Implement basic forecasting models
✓ Handle financial time series data in Python

1. Time Series Fundamentals

Understanding the basic components and characteristics of time series data.

import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose

# Load and process time series data
df = pd.read_csv('stock_data.csv', index_col='Date', parse_dates=True)
result = seasonal_decompose(df['Close'], period=252)  # 252 trading days

# Plot components
result.plot()

2. Moving Averages and Technical Analysis

Learn how to calculate and interpret moving averages for technical analysis.

# Calculate moving averages
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['EMA_20'] = df['Close'].ewm(span=20).mean()

# Generate trading signals
df['Signal'] = np.where(df['SMA_50'] > df['EMA_20'], 1, -1)

3. Time Series Forecasting

Introduction to forecasting methods using statistical models.

from statsmodels.tsa.holtwinters import ExponentialSmoothing

# Fit Holt-Winters model
model = ExponentialSmoothing(df['Close'],
                           seasonal_periods=252,
                           trend='add',
                           seasonal='add')
fitted_model = model.fit()

# Make predictions
forecast = fitted_model.forecast(30)  # 30-day forecast

💡 ChatGPT Learning Tips

Use these prompts to enhance your understanding of time series analysis:

"Explain how to decompose a financial time series into trend, seasonal, and residual components using Python"
"Show me how to calculate and interpret different types of moving averages for stock prices"
"Write code to implement a simple trading strategy using moving averages crossover"
"How can I use the statsmodels library to forecast stock prices?"
"What are the best practices for handling missing data in financial time series?"

📚 Research Tip: Use Perplexity.ai to search for "time series analysis Python financial data" or "stock price forecasting methods"

Weekly Project: Financial Time Series Analysis

Create a comprehensive time series analysis project that includes:

Data preprocessing and cleaning of financial time series data
Time series decomposition and visualization
Implementation of moving averages and technical indicators
Basic forecasting model using your choice of method
Performance evaluation and analysis

Use the provided code examples as a starting point and extend them with your own analysis.

Additional Resources

Investopedia: Introduction to Time Series Analysis - Great conceptual overview
Machine Learning Plus: Time Series Analysis in Python - Comprehensive Python tutorial
Forecasting: Principles and Practice (Online Textbook) - Advanced concepts
Statsmodels Time Series Analysis - Technical documentation
Pandas Time Series Documentation - Data handling reference
Kaggle Time Series Tutorial - Practical examples