Time Series Analysis, Forecasting, and Machine Learning
Time Series Analysis, Forecasting, and Machine Learning Course
Python for LSTMs, ARIMA, Deep Learning, Machine Learning, Support Vector Regression, +More in Time Series Forecasting
The Time Series Analysis, Forecasting, and Machine Learning course on Udemy is an excellent resource for anyone who wants to learn about time series analysis and forecasting. The course is designed for both beginners and experts, and it covers everything from the basics of time series analysis to advanced topics like deep learning, machine learning, and more.
The course is taught by the Lazy Programmer Team, who are experts in the field of data science. The course covers a wide range of topics, including ETS and Exponential Smoothing Models, Holt’s Linear Trend Model and Holt-Winters, Autoregressive and Moving Average Models (ARIMA), Seasonal ARIMA (SARIMA), SARIMAX, Auto ARIMA, Vector Autoregression and Moving Average Models (VARMA), machine learning models (including Logistic Regression, Support Vector Machines, and Random Forests), deep learning models (Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks), GRUs and LSTMs for Time Series Forecasting. You will also learn how to use Tensorflow 2 for predicting stock prices and returns.
The course is designed to be hands-on, so you’ll be able to apply what you learn to real-world problems. You’ll learn how to forecast sales data, stock prices and stock returns using time series analysis techniques. You’ll also learn how to classify smartphone data to predict user behavior.
Overall, the Time Series Analysis, Forecasting, and Machine Learning course on Udemy is an excellent resource for anyone who wants to learn about time series analysis and forecasting. It’s comprehensive, well-structured, and taught by experts in the field.
What you’ll learn in Time Series Analysis, Forecasting, and Machine Learning Course
- ETS and Exponential Smoothing Models
- Holt’s Linear Trend Model and Holt-Winters
- Autoregressive and Moving Average Models (ARIMA)
- Seasonal ARIMA (SARIMA), and SARIMAX
- Auto ARIMA
- The statsmodels Python library
- The pmdarima Python library
- Machine learning for time series forecasting
- Deep learning (ANNs, CNNs, RNNs, and LSTMs) for time series forecasting
- Tensorflow 2 for predicting stock prices and returns.
- Vector autoregression (VAR) and vector moving average (VMA) models (VARMA)
- AWS Forecast (Amazon’s time series forecasting service)
- FB Prophet (Facebook’s time series library)
- Modeling and forecasting financial time series.
- GARCH (volatility modeling)
Recommended Time Series Analysis Course
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Who this course is for:
- Anyone who loves or wants to learn about time series analysis
- Students and professionals who want to advance their career in finance, time series analysis, or data science
