Time Series Analysis, Forecasting, and Machine Learning

Time Series Analysis, Forecasting, and Machine Learning, available at $74.99, has an average rating of 4.75, with 176 lectures, based on 2391 reviews, and has 9143 subscribers.

You will learn about 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) This course is ideal for individuals who are Anyone who loves or wants to learn about time series analysis or Students and professionals who want to advance their career in finance, time series analysis, or data science It is particularly useful for Anyone who loves or wants to learn about time series analysis or Students and professionals who want to advance their career in finance, time series analysis, or data science.

Enroll now: Time Series Analysis, Forecasting, and Machine Learning

Summary

Title: Time Series Analysis, Forecasting, and Machine Learning

Price: $74.99

Average Rating: 4.75

Number of Lectures: 176

Number of Published Lectures: 174

Number of Curriculum Items: 176

Number of Published Curriculum Objects: 174

Original Price: $74.99

Quality Status: approved

Status: Live

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)

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: Warmup (Optional)

Chapter 2: Getting Set Up

Lecture 1: Where to get the code, notebooks, and data

Lecture 2: How to Succeed in This Course

Lecture 3: Temporary 403 Errors

Chapter 3: Time Series Basics

Lecture 1: Time Series Basics Section Introduction

Lecture 2: What is a Time Series?

Lecture 3: Modeling vs. Predicting

Lecture 4: Why Do We Care About Shapes?

Lecture 5: Types of Tasks

Lecture 6: Power, Log, and Box-Cox Transformations

Lecture 7: Power, Log, and Box-Cox Transformations in Code

Lecture 8: Forecasting Metrics

Lecture 9: Financial Time Series Primer

Lecture 10: Price Simulations in Code

Lecture 11: Random Walks and the Random Walk Hypothesis

Lecture 12: The Naive Forecast and the Importance of Baselines

Lecture 13: Naive Forecast and Forecasting Metrics in Code

Lecture 14: Time Series Basics Section Summary

Lecture 15: Suggestion Box

Chapter 4: Exponential Smoothing and ETS Methods

Lecture 1: Exponential Smoothing Section Introduction

Lecture 2: Exponential Smoothing Intuition for Beginners

Lecture 3: SMA Theory

Lecture 4: SMA Code

Lecture 5: EWMA Theory

Lecture 6: EWMA Code

Lecture 7: SES Theory

Lecture 8: SES Code

Lecture 9: Holt's Linear Trend Model (Theory)

Lecture 10: Holt's Linear Trend Model (Code)

Lecture 11: Holt-Winters (Theory)

Lecture 12: Holt-Winters (Code)

Lecture 13: Walk-Forward Validation

Lecture 14: Walk-Forward Validation in Code

Lecture 15: Application: Sales Data

Lecture 16: Application: Stock Predictions

Lecture 17: SMA Application: COVID-19 Counting

Lecture 18: SMA Application: Algorithmic Trading

Lecture 19: Exponential Smoothing Section Summary

Lecture 20: (Optional) More About State-Space Models

Chapter 5: ARIMA

Lecture 1: ARIMA Section Introduction

Lecture 2: Autoregressive Models – AR(p)

Lecture 3: Moving Average Models – MA(q)

Lecture 4: ARIMA

Lecture 5: ARIMA in Code

Lecture 6: Stationarity

Lecture 7: Stationarity in Code

Lecture 8: ACF (Autocorrelation Function)

Lecture 9: PACF (Partial Autocorrelation Function)

Lecture 10: ACF and PACF in Code (pt 1)

Lecture 11: ACF and PACF in Code (pt 2)

Lecture 12: Auto ARIMA and SARIMAX

Lecture 13: Model Selection, AIC and BIC

Lecture 14: Auto ARIMA in Code

Lecture 15: Auto ARIMA in Code (Stocks)

Lecture 16: ACF and PACF for Stock Returns

Lecture 17: Auto ARIMA in Code (Sales Data)

Lecture 18: How to Forecast with ARIMA

Lecture 19: Forecasting Out-Of-Sample

Lecture 20: ARIMA Section Summary

Chapter 6: Vector Autoregression (VAR, VMA, VARMA)

Lecture 1: Vector Autoregression Section Introduction

Lecture 2: VAR and VARMA Theory

Lecture 3: VARMA Code (pt 1)

Lecture 4: VARMA Code (pt 2)

Lecture 5: VARMA Code (pt 3)

Lecture 6: VARMA Econometrics Code (pt 1)

Lecture 7: VARMA Econometrics Code (pt 2)

Lecture 8: Granger Causality

Lecture 9: Granger Causality Code

Lecture 10: Converting Between Models (Optional)

Lecture 11: Vector Autoregression Section Summary

Chapter 7: Machine Learning Methods

Lecture 1: Machine Learning Section Introduction

Lecture 2: Supervised Machine Learning: Classification and Regression

Lecture 3: Autoregressive Machine Learning Models

Lecture 4: Machine Learning Algorithms: Linear Regression

Lecture 5: Machine Learning Algorithms: Logistic Regression

Lecture 6: Machine Learning Algorithms: Support Vector Machines

Lecture 7: Machine Learning Algorithms: Random Forest

Lecture 8: Extrapolation and Stock Prices

Lecture 9: Machine Learning for Time Series Forecasting in Code (pt 1)

Lecture 10: Forecasting with Differencing

Lecture 11: Machine Learning for Time Series Forecasting in Code (pt 2)

Lecture 12: Application: Sales Data

Lecture 13: Application: Predicting Stock Prices and Returns

Lecture 14: Application: Predicting Stock Movements

Lecture 15: Machine Learning Section Summary

Chapter 8: Deep Learning: Artificial Neural Networks (ANN)

Lecture 1: Artificial Neural Networks: Section Introduction

Lecture 2: The Neuron

Lecture 3: Forward Propagation

Lecture 4: The Geometrical Picture

Lecture 5: Activation Functions

Lecture 6: Multiclass Classification

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 15 votes
2 stars: 9 votes
3 stars: 41 votes
4 stars: 600 votes
5 stars: 1726 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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