Time Series Analysis, Forecasting, and Machine Learning

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

4.73 (2737 reviews)

Udemy

platform

English

language

Data Science

What you will learn

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)

Course Gallery

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Comidoc Review

Our Verdict

This 23.5 hour Time Series Analysis and Forecasting course on Udemy is led by an experienced and knowledgeable instructor who provides a thorough examination of various time-series analysis techniques—from exponential smoothing and ARIMA to machine learning and deep learning models. While some students might find specific mathematical concepts challenging without adequate prior exposure or intuitive understanding, the overall curriculum offers valuable insights and practical applications for data professionals eager to augment their time-series forecasting capabilities.

What We Liked

Comprehensive coverage of time series analysis techniques, including ETS, Holt's Linear Trend Model, ARIMA, GARCH, and deep learning methods
High-quality course materials, including videos and notebooks, providing a hands-on learning experience
Excellent communication skills of the instructor, explaining complex concepts clearly and breaking down complex topics for better understanding
Well-rounded curriculum with a strong emphasis on non-deep learning statistical models for time-series prediction

Potential Drawbacks

Some students may find it challenging to follow certain mathematical concepts without prior exposure or intuitive understanding, such as CNNs and GARCH
Lack of slides provided along with the videos might be inconvenient for some learners who prefer written summaries of the content
Limited focus on error or anomaly detection outside of Facebook Prophet compared to other time-series models
A small portion of students may find the instructor's approach condescending, potentially affecting their overall experience