Time Series & Forecasting

Level

Intermediate

Duration

2 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Master time series analysis and forecasting — from classical ARIMA models to modern deep learning approaches. Build systems that predict future values with confidence intervals, detect anomalies, and handle seasonality.

Time Series Basics: Trend, seasonality, stationarity, decomposition
Statistical Models: ARIMA, SARIMA, ETS, Holt-Winters
ML for Forecasting: Feature engineering, LightGBM, XGBoost
Deep Learning: LSTM, N-BEATS, Temporal Fusion Transformer
Anomaly Detection: Isolation forest, autoencoders for time series
Production: Forecast pipelines, evaluation metrics, deployment

Course Modules

📊 Week 1: Statistical & ML Forecasting▼

Time series properties: trend, seasonality, noise
Stationarity and differencing
ARIMA and SARIMA models
Exponential smoothing (Holt-Winters)
Feature engineering for time series (lags, rolling stats)
Lab 1: EDA and decomposition of time series
Lab 2: ARIMA for stock price forecasting
Lab 3: Seasonal SARIMA for retail demand
Lab 4: LightGBM with engineered time features
Lab 5: Multi-step forecasting strategies

🤖 Week 2: Deep Learning & Anomaly Detection▼

LSTM for sequence forecasting
N-BEATS and N-HiTS architecture
Temporal Fusion Transformer (TFT)
TimesFM and foundation models for time series
Anomaly detection techniques
Lab 6: LSTM energy consumption forecasting
Lab 7: N-BEATS with Nixtla library
Lab 8: Temporal Fusion Transformer
Lab 9: Time series anomaly detection
Lab 10 (Capstone): End-to-end forecasting system with dashboard

Prerequisites

Python (intermediate)
Pandas and NumPy familiarity
Basic ML knowledge (regression, evaluation)

Who Should Take This?

Data Scientists in finance, retail, or energy
ML Engineers building forecasting systems
Analysts leveling up from Excel to ML
Operations Researchers optimizing planning

Tools & Tech Stack

Python, pandas, statsmodels, NumPy
scikit-learn, LightGBM, XGBoost
PyTorch, pytorch-forecasting
Nixtla (N-BEATS, N-HiTS)
Plotly, Streamlit for dashboards

Ready to Start?

Turn historical data into confident predictions — and build systems that see what is coming next.

📧 Enroll Now

📈 Time Series & Forecasting