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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)

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

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!

Screenshots

Content

Welcome

Introduction and Outline

Where to Get the Code

Warmup (Optional)

Time Series Basics

Time Series Basics Section Introduction

What is a Time Series?

Modeling vs. Predicting

Why Do We Care About Shapes?

Types of Tasks

Power, Log, and Box-Cox Transformations

Power, Log, and Box-Cox Transformations in Code

Forecasting Metrics

Financial Time Series Primer

Price Simulations in Code

Random Walks and the Random Walk Hypothesis

The Naive Forecast and the Importance of Baselines

Naive Forecast and Forecasting Metrics in Code

Time Series Basics Section Summary

Suggestion Box

Exponential Smoothing and ETS Methods

Exponential Smoothing Section Introduction

Exponential Smoothing Intuition for Beginners

SMA Theory

SMA Code

EWMA Theory

EWMA Code

SES Theory

SES Code

Holt's Linear Trend Model (Theory)

Holt's Linear Trend Model (Code)

Holt-Winters (Theory)

Holt-Winters (Code)

Walk-Forward Validation

Walk-Forward Validation in Code

Application: Sales Data

Application: Stock Predictions

SMA Application: COVID-19 Counting

SMA Application: Algorithmic Trading

Exponential Smoothing Section Summary

ARIMA

ARIMA Section Introduction

Autoregressive Models - AR(p)

Moving Average Models - MA(q)

ARIMA

ARIMA in Code

Stationarity

Stationarity in Code

ACF (Autocorrelation Function)

PACF (Partial Autocorrelation Funtion)

ACF and PACF in Code (pt 1)

ACF and PACF in Code (pt 2)

Auto ARIMA and SARIMAX

Model Selection, AIC and BIC

Auto ARIMA in Code

Auto ARIMA in Code (Stocks)

ACF and PACF for Stock Returns

Auto ARIMA in Code (Sales Data)

How to Forecast with ARIMA

ARIMA Section Summary

Machine Learning Methods

Machine Learning Section Introduction

Supervised Machine Learning: Classification and Regression

Autoregressive Machine Learning Models

Machine Learning Algorithms: Linear Regression

Machine Learning Algorithms: Logistic Regression

Machine Learning Algorithms: Support Vector Machines

Machine Learning Algorithms: Random Forest

Extrapolation and Stock Prices

Machine Learning for Time Series Forecasting in Code (pt 1)

Forecasting with Differencing

Machine Learning for Time Series Forecasting in Code (pt 2)

Application: Sales Data

Application: Predicting Stock Prices and Returns

Application: Predicting Stock Movements

Machine Learning Section Summary

Deep Learning: Artificial Neural Networks (ANN)

Artificial Neural Networks: Section Introduction

The Neuron

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

ANN Code Preparation

Feedforward ANN for Time Series Forecasting Code

Human Activity Recognition Dataset

Human Activity Recognition: Code Preparation

Human Activity Recognition: Data Exploration

Human Activity Recognition: Multi-Input ANN

Human Activity Recognition: Feature-Based Model

Human Activity Recognition: Combined Model

How Does a Neural Network "Learn"?

Artificial Neural Networks: Section Summary

VIP: AWS Forecast

AWS Forecast Section Introduction

Data Model

Creating an IAM Role

Code pt 1 (Getting and Transforming the Data)

Code pt 2 (Uploading the data to S3)

Code pt 3 (Building your Model)

Code pt 4 (Generating and Evaluating the Forecast)

AWS Forecast Exercise

AWS Forecast Section Summary

Extras

Colab Notebooks

Setting Up Your Environment FAQ

Windows-Focused Environment Setup

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners FAQ

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Effective Learning Strategies for Machine Learning FAQ

How to Succeed in this Course (Long Version)

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

What is the Appendix?

BONUS: Where to get discount coupons and FREE deep learning material

Coupons

Date | Discount | Status | ||
---|---|---|---|---|

6/16/2021 | 75% OFF | Expired | ||

7/18/2021 | 75% OFF | Valid | ||

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