📈 Introduction to Time Series Analysis Series v1.0

Master practical time series analysis skills from fundamentals to advanced forecasting methods using ARIMA, LSTM, and Transformers

Series Overview

This series is a comprehensive practical educational content consisting of 5 chapters that progressively teaches time series analysis theory and implementation from the ground up.

Time Series Analysis is a technique for extracting trends and patterns from data observed along a time axis and predicting future values. You'll systematically learn a wide range of technologies, from time series-specific concepts such as stationarity, trends, and seasonality, to classical statistical models like AR, MA, and ARIMA, deep learning models including LSTM, GRU, and TCN, and even the latest Transformer-based methods such as Temporal Fusion Transformer and Informer. These skills are essential across various business and research fields including financial market price forecasting, demand forecasting, sensor data anomaly detection, and weather prediction. You'll understand and be able to implement time series forecasting technologies used in production by companies like Google, Amazon, and Uber. The series provides practical knowledge using major libraries such as statsmodels, Prophet, and PyTorch.

Features:

• ✅ Theory to Practice: Systematic learning from fundamental time series concepts to advanced forecasting methods
• ✅ Implementation-Focused: Over 40 executable Python/statsmodels/PyTorch code examples
• ✅ Business-Oriented: Practical forecasting methods designed for real business challenges
• ✅ Latest Technology: Implementation using ARIMA, LSTM, Transformer, and Informer
• ✅ Practical Applications: Hands-on practice with demand forecasting, anomaly detection, multivariate forecasting, and causal inference

Total Learning Time: 5-6 hours (including code execution and exercises)

How to Learn

Recommended Learning Order

For Beginners (completely new to time series analysis):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Duration: 5-6 hours

For Intermediate Learners (with machine learning experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 4-5 hours

For Topic-Specific Focus:
- Time series fundamentals/stationarity: Chapter 1 (focused study)
- ARIMA/SARIMA: Chapter 2 (focused study)
- LSTM/GRU: Chapter 3 (focused study)
- Transformers: Chapter 4 (focused study)
- Anomaly detection/causal inference: Chapter 5 (focused study)
- Duration: 60-80 minutes per chapter

Chapter Details

Chapter 1: Time Series Data Fundamentals

Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 8

Learning Content

1. What is Time Series Data - Definition, characteristics, application domains
2. Stationarity - Weak stationarity, strong stationarity, unit root tests
3. Trends and Seasonality - Detrending, seasonal adjustment, decomposition methods
4. Autocorrelation (ACF/PACF) - Autocorrelation function, partial autocorrelation function
5. Time Series Preprocessing - Missing value handling, outlier removal, normalization

Learning Objectives

• ✅ Understand characteristics of time series data
• ✅ Determine stationarity
• ✅ Decompose trends and seasonality
• ✅ Interpret ACF/PACF
• ✅ Properly preprocess time series data

Read Chapter 1 →

Chapter 2: Statistical Time Series Models

Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 10

Learning Content

1. Autoregressive Model (AR) - AR(p) model, coefficient estimation, order selection
2. Moving Average Model (MA) - MA(q) model, MA process characteristics
3. ARIMA Model - ARIMA(p,d,q), parameter selection, model diagnostics
4. Seasonal ARIMA Model (SARIMA) - SARIMA(p,d,q)(P,D,Q)s, seasonal periods
5. Prophet Model - Facebook trend forecasting, holiday effects

Learning Objectives

• ✅ Understand AR and MA models
• ✅ Build and evaluate ARIMA models
• ✅ Model data with seasonality
• ✅ Select appropriate model orders
• ✅ Perform practical forecasting with Prophet

Read Chapter 2 →

Chapter 3: Deep Learning for Time Series Forecasting

Difficulty: Intermediate to Advanced
Reading Time: 70-80 minutes
Code Examples: 9

Learning Content

1. RNN and LSTM - Recurrent neural networks, long-term dependencies, vanishing gradient problem
2. GRU (Gated Recurrent Unit) - Gating mechanism, comparison with LSTM
3. TCN (Temporal Convolutional Network) - Causal convolution, dilated convolution
4. Attention Mechanism - Attention weights, multi-head attention
5. Seq2Seq Model - Encoder-decoder, multi-step forecasting

Learning Objectives

• ✅ Understand LSTM and GRU mechanisms
• ✅ Implement time series forecasting with TCN
• ✅ Apply attention mechanisms
• ✅ Perform multi-step forecasting with Seq2Seq
• ✅ Tune model hyperparameters

Read Chapter 3 →

Chapter 4: Transformers for Time Series

Difficulty: Advanced
Reading Time: 70-80 minutes
Code Examples: 8

Learning Content

1. Transformer Fundamentals - Self-attention mechanism, positional encoding
2. Temporal Fusion Transformer (TFT) - Variable selection, multi-horizon forecasting
3. Informer - ProbSparse attention, efficient long-sequence forecasting
4. Autoformer - Auto-correlation mechanism, seasonal-trend decomposition
5. Implementation and Optimization - Batch processing, distributed training, inference acceleration

Learning Objectives

• ✅ Understand Transformer application to time series
• ✅ Implement complex forecasting tasks with TFT
• ✅ Optimize long-sequence forecasting with Informer
• ✅ Utilize state-of-the-art time series Transformers
• ✅ Optimize model computational efficiency

Read Chapter 4 →

Chapter 5: Time Series Applications

Difficulty: Advanced
Reading Time: 60-70 minutes
Code Examples: 9

Learning Content

1. Anomaly Detection - Statistical methods, deep learning, autoencoders
2. Multivariate Time Series Forecasting - VAR, VEC, multi-task learning
3. Causal Inference - Granger causality, structural equation models
4. Probabilistic Forecasting - Confidence intervals, quantile forecasting, Monte Carlo dropout
5. Business Applications - Demand forecasting, inventory optimization, price prediction

Learning Objectives

• ✅ Detect anomalies in time series
• ✅ Analyze multivariate time series
• ✅ Infer causal relationships
• ✅ Quantify uncertainty
• ✅ Apply time series analysis to business challenges

Read Chapter 5 →

Overall Learning Outcomes

Upon completing this series, you will acquire the following skills and knowledge:

Knowledge Level (Understanding)

• ✅ Explain fundamental time series concepts (stationarity, trends, seasonality)
• ✅ Understand differences between statistical and deep learning models
• ✅ Explain mechanisms of ARIMA, LSTM, and Transformers
• ✅ Understand evaluation metrics and interpretation for time series forecasting
• ✅ Explain anomaly detection and causal inference methods

Practical Skills (Doing)

• ✅ Properly preprocess and visualize time series data
• ✅ Implement forecasting with ARIMA models
• ✅ Perform deep learning forecasting with LSTM and Transformers
• ✅ Evaluate, compare, and select optimal models
• ✅ Implement anomaly detection and multivariate forecasting

Application Ability (Applying)

• ✅ Select appropriate time series methods for business challenges
• ✅ Build demand and price forecasting systems
• ✅ Design models according to time series characteristics
• ✅ Properly assess forecasting uncertainty
• ✅ Create practical time series analysis pipelines

Prerequisites

To learn this series effectively, it is desirable to have the following knowledge:

Required (Must Have)

• ✅ Python Fundamentals: Variables, functions, classes, NumPy, pandas
• ✅ Machine Learning Basics: Concepts of training, evaluation, and validation
• ✅ Statistics Basics: Mean, variance, correlation, probability distributions
• ✅ Linear Algebra: Vectors, matrices, matrix operations
• ✅ Calculus: Differentiation, gradients, optimization basics

Recommended (Nice to Have)

• 💡 Deep Learning Basics: Neural networks, loss functions, optimization
• 💡 PyTorch/TensorFlow: Experience with deep learning frameworks
• 💡 Statistical Time Series Analysis: Statistical time series course (recommended)
• 💡 Data Visualization: Matplotlib, seaborn
• 💡 Signal Processing: Fourier transform, filtering

Recommended Prior Learning:

• 📚 - ML fundamentals
• 📚 - NumPy, pandas
• 📚 Statistics Basics (Coming Soon) - Probability, statistical inference

Technologies and Tools

Main Libraries

• statsmodels 0.14+ - ARIMA, SARIMA, statistical time series analysis
• Prophet 1.1+ - Facebook time series forecasting
• PyTorch 2.0+ - Deep learning, LSTM, Transformer
• pandas 2.0+ - Data manipulation, time series processing
• NumPy 1.24+ - Numerical computing
• scikit-learn 1.3+ - Preprocessing, evaluation
• Matplotlib/seaborn 3.7+ - Visualization

Advanced Libraries

• PyTorch Forecasting 1.0+ - Temporal Fusion Transformer
• GluonTS 0.14+ - Amazon time series toolkit
• sktime 0.24+ - Time series machine learning
• tslearn 0.6+ - Time series clustering, classification

Development Environment

• Python 3.8+ - Programming language
• Jupyter Notebook - Interactive development
• CUDA 11.8+ - GPU acceleration (for deep learning)
• Git 2.40+ - Version control

Let's Get Started!

Are you ready? Begin with Chapter 1 and master time series analysis techniques!

Chapter 1: Time Series Data Fundamentals →

Next Steps

After completing this series, we recommend proceeding to the following topics:

Deep Dive Learning

• 📚 Advanced Time Series Models: N-BEATS, DeepAR, WaveNet
• 📚 Spatio-Temporal Data Analysis: Geographic time series, traffic forecasting
• 📚 Real-Time Time Series Processing: Streaming data, online learning
• 📚 Time Series Interpretability: SHAP, attention weight visualization

Related Series

• 🎯 Anomaly Detection (Coming Soon) - Deep dive into time series anomaly detection
• 🎯 Forecasting at Scale (Coming Soon) - Automated forecasting for thousands of series
• 🎯 Causal Inference (Coming Soon) - Causal analysis of time series

Practical Projects

• 🚀 Demand Forecasting System - Multi-step forecasting with retail data
• 🚀 Stock Price Forecasting Engine - Transformer forecasting for financial time series
• 🚀 IoT Anomaly Detection - Real-time monitoring of sensor data
• 🚀 Energy Consumption Forecasting - Seasonal modeling of power demand

Update History

• 2025-10-21: v1.0 Initial release

Your journey into time series analysis begins here!