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    <title>Week 9: Time Series Analysis in Finance</title>
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        <div class="key-concept">
            <h2>Week 9: Time Series Analysis in Finance</h2>
            <p class="lead">Master the techniques of time series analysis for financial data, from basic trend analysis to advanced forecasting methods.</p>
        </div>

        <h3>Learning Objectives:</h3>
        <ul>
            <li>✓ Understand the components of time series data (trend, seasonality, noise)</li>
            <li>✓ Learn techniques for time series decomposition and analysis</li>
            <li>✓ Apply moving averages and exponential smoothing</li>
            <li>✓ Implement basic forecasting models</li>
            <li>✓ Handle financial time series data in Python</li>
        </ul>

        <div class="resource-link">
            <h4>1. Time Series Fundamentals</h4>
            <p>Understanding the basic components and characteristics of time series data.</p>
            <div class="code-example">
                <pre>
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()</pre>
            </div>
        </div>

        <div class="resource-link">
            <h4>2. Moving Averages and Technical Analysis</h4>
            <p>Learn how to calculate and interpret moving averages for technical analysis.</p>
            <div class="code-example">
                <pre>
# 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)</pre>
            </div>
        </div>

        <div class="resource-link">
            <h4>3. Time Series Forecasting</h4>
            <p>Introduction to forecasting methods using statistical models.</p>
            <div class="code-example">
                <pre>
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</pre>
            </div>
        </div>

        <div class="chatgpt-tip">
            <h4>💡 ChatGPT Learning Tips</h4>
            <p>Use these prompts to enhance your understanding of time series analysis:</p>
            <ol>
                <li>"Explain how to decompose a financial time series into trend, seasonal, and residual components using Python"</li>
                <li>"Show me how to calculate and interpret different types of moving averages for stock prices"</li>
                <li>"Write code to implement a simple trading strategy using moving averages crossover"</li>
                <li>"How can I use the statsmodels library to forecast stock prices?"</li>
                <li>"What are the best practices for handling missing data in financial time series?"</li>
            </ol>
            <div class="research-tip">
                <p>📚 <strong>Research Tip:</strong> Use <a href="https://www.perplexity.ai/" target="_blank">Perplexity.ai</a> to search for "time series analysis Python financial data" or "stock price forecasting methods"</p>
            </div>
        </div>

        <div class="exercise">
            <h4>Weekly Project: Financial Time Series Analysis</h4>
            <p>Create a comprehensive time series analysis project that includes:</p>
            <ol>
                <li>Data preprocessing and cleaning of financial time series data</li>
                <li>Time series decomposition and visualization</li>
                <li>Implementation of moving averages and technical indicators</li>
                <li>Basic forecasting model using your choice of method</li>
                <li>Performance evaluation and analysis</li>
            </ol>
            <p>Use the provided code examples as a starting point and extend them with your own analysis.</p>
        </div>

        <h3>Additional Resources</h3>
        <ul>
            <li><a href="https://www.investopedia.com/terms/t/timeseries.asp" target="_blank">Investopedia: Introduction to Time Series Analysis</a> - Great conceptual overview</li>
            <li><a href="https://www.machinelearningplus.com/time-series/time-series-analysis-python/" target="_blank">Machine Learning Plus: Time Series Analysis in Python</a> - Comprehensive Python tutorial</li>
            <li><a href="https://otexts.com/fpp3/" target="_blank">Forecasting: Principles and Practice (Online Textbook)</a> - Advanced concepts</li>
            <li><a href="https://www.statsmodels.org/stable/tsa.html" target="_blank">Statsmodels Time Series Analysis</a> - Technical documentation</li>
            <li><a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html" target="_blank">Pandas Time Series Documentation</a> - Data handling reference</li>
            <li><a href="https://www.kaggle.com/code/thebrownviking20/everything-you-can-do-with-a-time-series" target="_blank">Kaggle Time Series Tutorial</a> - Practical examples</li>
        </ul>
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