UDUdemy
Time Series Analysis and Forecasting with Python
Intermediate 11 hours English Completion Certificate Certificate
About this course
This course covers time series analysis and forecasting from classical methods to deep learning: decomposition, stationarity testing, ARIMA and SARIMA models, Facebook Prophet for trend and seasonality modeling, and LSTM neural networks for sequence forecasting.
Students apply each method to real datasets — retail demand, stock prices, energy consumption — and learn to evaluate forecast quality with proper metrics and backtesting procedures.
What you'll learn
Decompose time series into trend, seasonality, and residual components
Test for stationarity and apply differencing to prepare series for modeling
Build ARIMA and SARIMA models with proper parameter selection
Use Facebook Prophet for automatic trend and seasonality modeling
Apply LSTM neural networks to multi-step time series forecasting
This course includes
11h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
FK
Frank Kane
Udemy instructor
80K+ learners12 courses4.4 instructor rating
Taught by data science practitioners on Udemy specializing in applied forecasting for business and operational use cases.
Requirements
- Python and pandas experience; basic statistics knowledge helpful
Who this course is for
- Data scientists who need forecasting for business applications
- Analysts working on demand planning, inventory, or financial forecasting
- ML practitioners who want time series as a specialization
About this provider
UD
Udemy
The world's largest online learning marketplace. 65M+ students, 210,000+ courses.
Frequently asked questions
Classical methods often outperform deep learning for short series with clear seasonality; LSTM shines with long series and multiple covariates.
Prophet is covered thoroughly; newer libraries like NeuralProphet are mentioned as extensions.