Time Series Analysis, Forecasting, and Machine Learning

Name: Time Series Analysis, Forecasting, and Machine Learning
Price: 79.99 USD
Availability: InStock
Rating: 4.7 (3183 reviews)

4.7(3,183)13k studentsINTERMEDIATEEnglish

Korshub's take

Beyond the classic models

Time series analysis usually starts with exponential smoothing and ARIMA, and this course covers both families, from ETS and Holt's trend model through Holt-Winters to the SARIMAX variants. It then moves into machine learning and deep learning, with recurrent networks such as GRUs and LSTMs used for forecasting.

Applications

Forecasting sales data
Forecasting stock returns
Classifying smartphone data as a way to anticipate user behavior

Who should take it

The course is pitched at intermediate learners. Mixing classical statistical models with modern neural methods makes it useful for anyone who needs to forecast something with a time element and wants to compare approaches instead of relying on one. Some familiarity with Python will help.

About this course

Hello friends!

Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.

Time Series Analysis has become an especially important field in recent years.

With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

ETS and Exponential Smoothing
Holt's Linear Trend Model
Holt-Winters Model
ARIMA, SARIMA, SARIMAX, and Auto ARIMA
ACF and PACF
Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

Time series forecasting of sales data
Time series forecasting of stock prices and stock returns
Time series classification of smartphone data to predict user behavior

The VIP version of the course will cover even more exciting topics, such as:

AWS Forecast (Amazon's state-of-the-art low-code forecasting API)
GARCH (financial volatility modeling)
FB Prophet (Facebook's time series library)

So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

Thanks for reading, and I'll see you in class!

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out