Time Series Analytics

A Comprehensive Course for Financial Data Analysis

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Table of Contents

1. Course Overview
2. Prerequisites
3. Installation & Setup
4. Course Structure
5. Complete Topic Reference
6. How to Use These Materials
7. Quick Start Guide
8. Learning Path Recommendations
9. Troubleshooting
10. Additional Resources

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Course Overview

This course provides a hands-on, practical approach to Time Series Analytics with a focus on financial data analysis. Through real-world examples using Bitcoin, Gold, and stock price data, students will learn to:

• Understand and visualize time series patterns
• Build forecasting models (ARIMA, SARIMA, Exponential Smoothing)
• Model volatility using GARCH
• Analyze multiple time series with VAR models
• Validate and compare model performance
• Create interactive visualizations for data exploration

Course Philosophy

Theory → Implementation → Interpretation → Application

Each topic follows this pattern:

1. Concept explanation with mathematical formulas
2. Working Python code with real financial data
3. Output interpretation with practical insights
4. Visualization for better understanding

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Prerequisites

Required Knowledge

Area	Topics	Level
Statistics	Mean, variance, correlation, hypothesis testing	Intermediate
Mathematics	Basic calculus, linear algebra fundamentals	Basic
Python	Variables, loops, functions, pandas basics	Intermediate
Finance	Understanding of prices, returns, volatility	Basic

Recommended (Not Required)

• Prior exposure to regression analysis
• Familiarity with Jupyter notebooks
• Basic understanding of financial markets

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Installation & Setup

Option 1: Google Colab (Recommended)

No installation required! Simply:

1. Go to Google Colab
2. Upload the .ipynb notebook files
3. Run the first cell to install dependencies

# Run this in the first cell of any notebook
!pip install yfinance pmdarima arch plotly --quiet

Option 2: Local Installation

# Create a new directory for your project
mkdir timeseries_analytics
cd timeseries_analytics

# Create a virtual environment
python -m venv venv

# Activate it
# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate

# Install required packages
pip install yfinance pandas numpy matplotlib seaborn statsmodels scipy pmdarima arch plotly jupyter

Package Dependencies

Package	Version	Purpose
yfinance	≥0.2.0	Download financial data
pandas	≥1.5.0	Data manipulation
numpy	≥1.21.0	Numerical computing
matplotlib	≥3.5.0	Static visualizations
seaborn	≥0.12.0	Statistical visualizations
statsmodels	≥0.14.0	Time series models (ARIMA, VAR)
scipy	≥1.9.0	Statistical functions
pmdarima	≥2.0.0	Auto ARIMA
arch	≥5.0.0	GARCH models
plotly	≥5.0.0	Interactive visualizations

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Course Structure

Time Series Analytics/
│
├── 📓 Jupyter Notebooks (Complete Modules)
│   ├── module3_forecasting_model_selection.ipynb
│   ├── module4_advanced_models.ipynb
│   └── module5_integration_practice.ipynb
│
├── 📜 Python Scripts (Original Modules)
│   ├── module3_forecasting_model_selection.py
│   ├── module4_advanced_models.py
│   └── module5_integration_practice.py
│
├── 📁 individual_topics/ (20 Self-Contained Scripts)
│   ├── topic_3_1_train_test_split.py
│   ├── topic_3_2_ar_model.py
│   ├── ... (17 more files)
│   └── topic_5_3_interactive_plotly.py
│
└── 📖 README.md (This file)

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Complete Topic Reference

Module 3: Forecasting and Model Selection

#	Topic	Key Concepts	Notebook	Script	Time
3.1	Train-Test Split	Chronological split, data leakage prevention	module3_*.ipynb	topic_3_1_train_test_split.py	15 min
3.2	AR Model	Autoregressive, momentum, φ coefficients	module3_*.ipynb	topic_3_2_ar_model.py	20 min
3.3	MA Model	Moving average, shock effects, θ coefficients	module3_*.ipynb	topic_3_3_ma_model.py	20 min
3.4	ARMA Selection	AIC, BIC, model comparison	module3_*.ipynb	topic_3_4_arma_selection.py	25 min
3.5	ARIMA	Non-stationarity, differencing, ADF test	module3_*.ipynb	topic_3_5_arima_nonstationary.py	25 min
3.6	Auto ARIMA	Automatic parameter selection, pmdarima	module3_*.ipynb	topic_3_6_auto_arima.py	15 min
3.7	Residual Diagnostics	White noise, Ljung-Box test, Q-Q plot	module3_*.ipynb	topic_3_7_residual_diagnostics.py	25 min
3.8	Rolling Validation	Walk-forward, RMSE, MAE	module3_*.ipynb	topic_3_8_rolling_validation.py	20 min
3.9	Prediction Intervals	Confidence bands, uncertainty quantification	module3_*.ipynb	topic_3_9_prediction_intervals.py	20 min

Module 3 Total Time: ~3 hours

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Module 4: Advanced Time Series Models

#	Topic	Key Concepts	Notebook	Script	Time
4.1	SARIMA	Seasonal patterns, (P,D,Q,s) order	module4_*.ipynb	topic_4_1_sarima.py	25 min
4.2	Simple Exp. Smoothing	Alpha parameter, weighted averages	module4_*.ipynb	topic_4_2_simple_exp_smoothing.py	20 min
4.3	Holt's Linear	Trend component, level + slope	module4_*.ipynb	topic_4_3_holt_linear.py	20 min
4.4	Holt-Winters	Seasonality, additive vs multiplicative	module4_*.ipynb	topic_4_4_holt_winters.py	25 min
4.5	VAR Model	Multivariate, Granger causality	module4_*.ipynb	topic_4_5_var_model.py	30 min
4.6	ARCH Detection	Volatility clustering, LM test	module4_*.ipynb	topic_4_6_arch_detection.py	20 min
4.7	GARCH	Conditional volatility, α + β	module4_*.ipynb	topic_4_7_garch.py	30 min
4.8	Model Comparison	Multi-criteria evaluation, dashboards	module4_*.ipynb	topic_4_8_model_comparison.py	25 min

Module 4 Total Time: ~3.5 hours

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Module 5: Integration and Practice

#	Topic	Key Concepts	Notebook	Script	Time
5.1	Complete Pipeline	8-step workflow, end-to-end analysis	module5_*.ipynb	topic_5_1_complete_pipeline.py	45 min
5.2	Reusable Functions	Modular code, compare_models()	module5_*.ipynb	topic_5_2_reusable_function.py	30 min
5.3	Interactive Viz	Plotly, hover, zoom, exploration	module5_*.ipynb	topic_5_3_interactive_plotly.py	25 min

Module 5 Total Time: ~1.5 hours

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Course Summary

Module	Topics	Total Time	Focus
Module 3	9	~3 hours	Fundamentals & Forecasting
Module 4	8	~3.5 hours	Advanced Models
Module 5	3	~1.5 hours	Integration & Practice
Total	20	~8 hours	Complete Course

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How to Use These Materials

Option A: Complete Notebooks (Recommended for Structured Learning)

Best for: Classroom instruction, sequential learning, comprehensive understanding

1. Open Google Colab
2. Upload the module notebook (.ipynb file)
3. Run cells sequentially from top to bottom
4. Read explanations, run code, observe outputs
5. Complete all topics in a module before moving on

📓 module3_forecasting_model_selection.ipynb
   ├── Setup cell (run first)
   ├── Topic 3.1: Train-Test Split
   ├── Topic 3.2: AR Model
   ├── ... (all topics in sequence)
   └── Module Summary

Advantages:

• Logical flow from basic to advanced
• All dependencies handled in setup
• Rich markdown explanations
• Summary tables for review

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Option B: Individual Topic Scripts (Recommended for Quick Reference)

Best for: Quick review, specific topic lookup, copy-paste examples

1. Navigate to individual_topics/ folder
2. Find the specific topic you need
3. Copy the entire script
4. Paste into Google Colab or Python IDE
5. Run and modify as needed

# Example: Just want to learn GARCH?
# Copy contents of topic_4_7_garch.py into Colab

Advantages:

• Self-contained (no dependencies on other files)
• Under 40 lines of code each
• Heavy comments for quick understanding
• Perfect for exam review

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Usage Comparison

Feature	Complete Notebooks	Individual Scripts
Best for	Full learning	Quick reference
Setup	Once per module	Each script standalone
Learning style	Sequential	Topic-specific
Code length	Comprehensive	Minimal (~35 lines)
Comments	Markdown cells	Inline comments
Time investment	Hours	Minutes

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Quick Start Guide

5-Minute Quick Start

# Step 1: Open Google Colab (colab.research.google.com)

# Step 2: Run this setup cell
!pip install yfinance statsmodels --quiet
import yfinance as yf
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA

# Step 3: Download data
btc = yf.download('BTC-USD', start='2020-01-01', progress=False)['Close'].dropna()

# Step 4: Split data (80% train, 20% test)
train = btc[:int(len(btc)*0.8)]
test = btc[int(len(btc)*0.8):]

# Step 5: Fit ARIMA model
model = ARIMA(train, order=(1, 1, 1))
fitted = model.fit()

# Step 6: Forecast and plot
forecast = fitted.forecast(steps=len(test))
plt.figure(figsize=(12, 5))
plt.plot(train[-100:], label='Training')
plt.plot(test, label='Actual Test')
plt.plot(test.index, forecast, label='Forecast', linestyle='--')
plt.legend()
plt.title('Your First Time Series Forecast!')
plt.show()

print(f"Model AIC: {fitted.aic:.2f}")
print("Congratulations! You've built your first forecasting model!")

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Learning Path Recommendations

Path 1: Complete Beginner (No Prior Experience)

Week 1: Module 3 (Topics 3.1 - 3.5)
        Focus: Fundamentals, stationarity, basic ARIMA

Week 2: Module 3 (Topics 3.6 - 3.9)
        Focus: Model selection, diagnostics, validation

Week 3: Module 4 (Topics 4.1 - 4.4)
        Focus: Exponential smoothing family

Week 4: Module 4 (Topics 4.5 - 4.8)
        Focus: VAR, GARCH, model comparison

Week 5: Module 5 (All Topics)
        Focus: Integration, practice, projects

Path 2: Intermediate (Some Statistics Background)

Day 1: Module 3 (Complete)
       Skim 3.1-3.3, focus on 3.4-3.9

Day 2: Module 4 (Complete)
       Focus on GARCH (4.6-4.7) and VAR (4.5)

Day 3: Module 5 + Practice
       Build your own analysis pipeline

Path 3: Quick Review (Exam Preparation)

Session 1 (2 hours): Individual scripts 3.1-3.9
Session 2 (2 hours): Individual scripts 4.1-4.8
Session 3 (1 hour):  Individual scripts 5.1-5.3

Path 4: Project-Focused

Start: Topic 5.1 (Complete Pipeline)
Then:  Pick specific topics as needed
       - Need GARCH? → Topic 4.7
       - Need seasonality? → Topics 4.1, 4.4
       - Need multivariate? → Topic 4.5

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Troubleshooting

Common Issues and Solutions

1. Package Installation Errors

# Problem: "ModuleNotFoundError: No module named 'yfinance'"
# Solution: Install the package
!pip install yfinance --quiet

# Problem: "arch" package installation fails
# Solution: Try with specific version
!pip install arch==5.3.1 --quiet

2. Data Download Issues

# Problem: yfinance returns empty data
# Solution 1: Check internet connection
# Solution 2: Try different date range
btc = yf.download('BTC-USD', start='2022-01-01', progress=False)

# Solution 3: Add error handling
try:
    data = yf.download('BTC-USD', start='2020-01-01', progress=False)['Close']
    if data.empty:
        print("No data returned. Check ticker symbol and date range.")
except Exception as e:
    print(f"Error: {e}")

3. Model Convergence Issues

# Problem: "Model did not converge"
# Solution 1: Try simpler model
model = ARIMA(data, order=(1, 1, 0))  # Simpler than (2, 1, 2)

# Solution 2: Use different optimizer
model = ARIMA(data, order=(1, 1, 1))
fitted = model.fit(method='powell')

# Solution 3: Scale your data
data_scaled = (data - data.mean()) / data.std()

4. Memory Issues in Colab

# Problem: "ResourceExhaustedError" or Colab crashes
# Solution 1: Use smaller dataset
data = data[-500:]  # Last 500 observations only

# Solution 2: Clear memory
import gc
gc.collect()

# Solution 3: Restart runtime (Runtime → Restart runtime)

5. Plotting Issues

# Problem: Plots not showing
# Solution 1: Use plt.show()
plt.plot(data)
plt.show()

# Solution 2: For Colab, use inline magic
%matplotlib inline

# Problem: Plotly not displaying
# Solution: Use Colab-specific renderer
import plotly.io as pio
pio.renderers.default = 'colab'

6. Date/Time Issues

# Problem: "DatetimeIndex has no attribute 'date'"
# Solution: Access index correctly
print(data.index[0])  # Not data.index[0].date() for some versions

# Problem: Forecast index doesn't align
# Solution: Create proper date range
forecast_index = pd.date_range(
    start=train.index[-1] + pd.Timedelta(days=1),
    periods=30,
    freq='D'  # or 'B' for business days
)

Getting Help

1. Check the error message carefully - Python errors are usually descriptive
2. Search the error on Stack Overflow
3. Check package documentation:
   • statsmodels
   • pmdarima
   • arch
4. Simplify your code - isolate the problem

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Additional Resources

Books

• Forecasting: Principles and Practice by Hyndman & Athanasopoulos (free online)
• Time Series Analysis by Hamilton
• Analysis of Financial Time Series by Tsay

Online Resources

• Statsmodels Documentation
• pmdarima Documentation
• ARCH Package Documentation

Video Tutorials

• Search "ARIMA Python tutorial" on YouTube
• Coursera: "Practical Time Series Analysis"
• DataCamp: "Time Series with Python"

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File Checklist

Jupyter Notebooks

• module3_forecasting_model_selection.ipynb
• module4_advanced_models.ipynb
• module5_integration_practice.ipynb

Individual Topic Scripts (in individual_topics/)

• topic_3_1_train_test_split.py
• topic_3_2_ar_model.py
• topic_3_3_ma_model.py
• topic_3_4_arma_selection.py
• topic_3_5_arima_nonstationary.py
• topic_3_6_auto_arima.py
• topic_3_7_residual_diagnostics.py
• topic_3_8_rolling_validation.py
• topic_3_9_prediction_intervals.py
• topic_4_1_sarima.py
• topic_4_2_simple_exp_smoothing.py
• topic_4_3_holt_linear.py
• topic_4_4_holt_winters.py
• topic_4_5_var_model.py
• topic_4_6_arch_detection.py
• topic_4_7_garch.py
• topic_4_8_model_comparison.py
• topic_5_1_complete_pipeline.py
• topic_5_2_reusable_function.py
• topic_5_3_interactive_plotly.py

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License

This material is for educational purposes. Feel free to use, modify, and share for learning.

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Contact

For questions or feedback about the course materials, please contact the course instructor.

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Last Updated: December 2024

Created for Time Series Analytics Course @ BITS