Applied Time Series Analysis and Forecasting in Python
Learn time series analysis with Python! Master forecasting techniques: ARIMA, SARIMAX, GARCH, vector models & more. Practical skills for data prediction.
Course Content
Applied Time Series Analysis and Forecasting in Python is an all-levels Udemy course taught by Akhil Vydyula. It runs 8h 28m, is taught in English, and includes a certificate of completion. Learners rate it 4.2 out of 5 from 19 reviews, with 9,147 students enrolled. It is a paid course, first published January 2023.
Unlock Your Career in Finance & Data Science with Time Series Analysis in Python
Are you ready to master the art of predicting future trends? This comprehensive course equips you with the practical skills to excel as a Quantitative Finance Analyst, Data Analyst, or Data Scientist. Learn to forecast financial performance, estimate portfolio risk, and predict real estate values using powerful time series analysis techniques.
What You'll Learn:
- Fundamental Time Series Theory: Understand the core concepts behind time series modeling, including stationarity, seasonality, white noise, random walks, and autocorrelation (ACF) and partial autocorrelation functions (PACF).
- Statistical Models: Dive deep into widely used statistical models for forecasting:
- AR (Autoregressive Model)
- MA (Moving Average Model)
- ARMA (Autoregressive Moving Average Model)
- ARIMA (Autoregressive Integrated Moving Average Model)
- ARIMAX (ARIMA with Exogenous Variables)
- SARIMA (Seasonal ARIMA Model)
- SARIMAX (Seasonal ARIMA with Exogenous Variables)
- ARCH (Autoregressive Conditional Heteroscedasticity Model)
- GARCH (Generalized Autoregressive Conditional Heteroscedasticity Model)
- VARMA (Vector Autoregressive Moving Average Model)
- VAR (Vector Autoregression)
- VARMAX (Vector Autoregressive Moving Average Model with Exogenous Variables)
- Python Implementation: Gain hands-on experience using Python and key libraries:
- Pandas: Leverage the powerful time series functionality built into Pandas.
- NumPy: Fundamental numerical computing.
- Matplotlib: Data visualization.
- StatsModels: Statistical modeling.
- Finance: Financial data and analysis.
- ARCH & Prima: Advanced time series modeling.
- Deep Learning for Time Series: Explore cutting-edge deep learning techniques with TensorFlow:
- Simple Linear Model (1-layer Neural Network)
- DNN (Deep Neural Network)
- CNN (Convolutional Neural Network)
- LSTM (Long Short-Term Memory)
- CNN + LSTM Models
- ResNet (Residual Networks)
- Autoregressive LSTM
- Model Selection: Master model selection techniques using AIC (Akaike's Information Criterion).
- End-to-End Projects: Complete more than 5 practical projects in Python, with all source code provided.
Why Choose This Course?
- Practical & Applied: Focus on real-world applications of time series analysis.
- Comprehensive & Easy to Understand: A well-structured course designed for learners of all levels.
- Up-to-Date: Combines the latest statistical and deep learning techniques.
- Extensive Resources: Includes notebook files, course notes, quiz questions, and numerous exercises.
- Career-Focused: Develop the skills employers are looking for in data science and quantitative finance.
Who is this course for?
This course is perfect for anyone looking to:
- Learn time series forecasting
- Advance their career in data science or quantitative finance
- Develop practical data analysis skills
- Master Python for financial modeling
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