📊 Data Science Week 1 of 14 BSc · Y3 S2 ⏱ ~50 min

Week 1: ARIMA Models, Stationarity & Forecasting

Master ARIMA modeling, LSTM for sequences, anomaly detection algorithms, and forecasting for real business applications.

University of Aliens

DS304 — Lecture 1 · BSc Y3 S2

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed

🎯 Learning Objectives

Test for and achieve stationarity using differencing and transformations
Fit ARIMA and SARIMA models with ACF/PACF analysis
Build LSTM-based sequence models for time series
Detect anomalies using statistical and ML approaches

Topics Covered This Lecture

Stationarity & Differencing

ARIMA: AR, I, MA Components

Seasonal Decomposition (STL)

LSTM for Sequences & Anomaly Detection

📖 Lecture Overview

This first lecture establishes the foundational framework for Time Series Analysis. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Master ARIMA modeling, LSTM for sequences, anomaly detection algorithms, and forecasting for real business applications. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Stationarity & Differencing, ARIMA: AR, I, MA Components, Seasonal Decomposition (STL), LSTM for Sequences & Anomaly Detection. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for DS304
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Stationarity & Differencing and ARIMA: AR, I, MA Components. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

DS304 Project 1: Sales Forecasting System

Build a forecasting system for monthly retail sales data. Compare ARIMA, Prophet, and LSTM approaches. Evaluate with MAPE, RMSE, and directional accuracy.

Stationarity tests (ADF, KPSS) and transformations
ARIMA model with ACF/PACF justification
LSTM sequence model implementation
Forecast comparison dashboard with confidence intervals

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

What does the Augmented Dickey-Fuller test check? What does it mean to fail the test?

Analysis Short Answer

In ARIMA(p,d,q), what do the three parameters represent?

Applied Code / Proof

Why might LSTM outperform ARIMA for a time series with non-linear seasonal patterns?