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# Time Series Analysis, Forecasting, and Machine Learning

## Time Series Analysis, Forecasting, and Machine Learning

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

#### What you’ll learn

Time Series Analysis, Forecasting, and Machine Learning

• 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 stats models 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)

#### Requirements

• Decent Python coding skills
• Matrix arithmetic
• Probability

#### Description

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
• The Time series forecasting of stock prices and stock returns
• Time series classification of smartphone data to predict user behaviour

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 modelling)
• 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!

#### 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
• Last updated 7/2021

#### Time Series Analysis, Forecasting, and Machine Learning

Marketing Analytics: Forecasting Models with Excel

Content From: https://www.udemy.com/course/time-series-analysis/