Time Series Analysis, Forecasting, And Machine Learning

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.