Applied Time Series Analysis in Python

Applied Time Series Analysis in Python, available at $69.99, has an average rating of 4.3, with 43 lectures, based on 805 reviews, and has 3345 subscribers.

You will learn about Descriptive vs inferential statistics Random walk model Moving average model Autoregression ACF and PACF Stationarity ARIMA, SARIMA, SARIMAX VAR, VARMA, VARMAX Apply deep learning for time series analysis with Tensorflow Linear models, DNN, LSTM, CNN, ResNet Automate time series analysis with Prophet This course is ideal for individuals who are Beginner data scientists looking to gain experience with time series or Deep learning beginners curious about times series or Professional data scientists who need to analyze time series or Data scientists looking to transition from R to Python It is particularly useful for Beginner data scientists looking to gain experience with time series or Deep learning beginners curious about times series or Professional data scientists who need to analyze time series or Data scientists looking to transition from R to Python.

Enroll now: Applied Time Series Analysis in Python

Summary

Title: Applied Time Series Analysis in Python

Price: $69.99

Average Rating: 4.3

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Descriptive vs inferential statistics
Random walk model
Moving average model
Autoregression
ACF and PACF
Stationarity
ARIMA, SARIMA, SARIMAX
VAR, VARMA, VARMAX
Apply deep learning for time series analysis with Tensorflow
Linear models, DNN, LSTM, CNN, ResNet
Automate time series analysis with Prophet

Who Should Attend

Beginner data scientists looking to gain experience with time series
Deep learning beginners curious about times series
Professional data scientists who need to analyze time series
Data scientists looking to transition from R to Python

Target Audiences

Beginner data scientists looking to gain experience with time series
Deep learning beginners curious about times series
Professional data scientists who need to analyze time series
Data scientists looking to transition from R to Python

This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:

stationarity and augmented Dicker-Fuller test
seasonality
white noise
random walk
autoregression
moving average
ACF and PACF,
Model selection with AIC (Akaike’s Information Criterion)

Then, we move on and apply more complex statistical models for time series forecasting:

ARIMA (Autoregressive Integrated Moving Average model)
SARIMA (Seasonal Autoregressive Integrated Moving Average model)
SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)

We also cover multiple time series forecasting with:

VAR (Vector Autoregression)
VARMA (Vector Autoregressive Moving Average model)
VARMAX (Vector Autoregressive Moving Average model with exogenous variable)

Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:

Simple linear model (1 layer neural network)
DNN (Deep Neural Network)
CNN (Convolutional Neural Network)
LSTM (Long Short-Term Memory)
CNN + LSTM models
ResNet (Residual Networks)
Autoregressive LSTM

Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What are Time Series?

Chapter 2: Statistical Learning Approach: The Building Blocks

Lecture 1: Basic Statistics

Lecture 2: Setup for coding exercises

Lecture 3: Coding Exercise: Descriptive and Inferential Statistics

Lecture 4: Autocorrelation and White Noise

Lecture 5: Stationarity and Differencing

Chapter 3: Statistical Learning Approach: Basic Models

Lecture 1: Random Walk

Lecture 2: Coding Excercise: Random Walk

Lecture 3: Moving Average Model

Lecture 4: Coding Exercise: Moving Average Model

Lecture 5: Autoregressive Model

Lecture 6: Mini Project: Autoregressive Model

Lecture 7: ARMA: Autoregressive Moving Average Model'

Lecture 8: Coding Exercise: ARMA

Chapter 4: Statistical Learning Approach: Advanced Models

Lecture 1: ARIMA: Autoregressive Integrated Moving Average Model

Lecture 2: Project 1: ARIMA

Lecture 3: SARIMA

Lecture 4: Project 2: SARIMA

Lecture 5: AIC: Akaike Information Criterion

Lecture 6: SARIMAX

Lecture 7: Project 3: SARIMAX

Lecture 8: General Modelling Procedure

Lecture 9: VAR: Vector Autoregressions

Lecture 10: Project 4 – Part 1: VAR

Lecture 11: Project 4 – Part 2: VARMA

Lecture 12: Project 4 – Part 3: VARMAX

Chapter 5: Deep Learning Approach: Theory

Lecture 1: Introduction

Lecture 2: Deep Neural Networks (DNN)

Lecture 3: Recurrent Neural Network and Long Short-Term Memory (RNN and LSTM)

Lecture 4: Convolutional Neural Networks (CNN)

Chapter 6: Deep Learning Approach: End-to-end Project

Lecture 1: Project 5 – Part 1: Initial setup

Lecture 2: Project 5 – Part 2: Exploratory Data Analysis (EDA)

Lecture 3: Project 5 – Part 3: Feature Engineering

Lecture 4: Project 5 – Part 4: Data Windowing and Training Function

Lecture 5: Project 5 – Part 5: Single Step Models

Lecture 6: Project 5 – Part 6: Multi Output Models

Lecture 7: Project 5 – Part 7: Multi Step Models

Chapter 7: Conclusion and References

Lecture 1: Congratulations and Thank You!

Lecture 2: References

Chapter 8: Bonus: Automated Time Series Analysis with Prophet

Lecture 1: Introduction to Prophet

Lecture 2: Working with Prophet

Lecture 3: Project: Predict Bus Ridership with Prophet

Instructors

Marco Peixeiro
Data Scientist and Instructor

Rating Distribution

1 stars: 13 votes
2 stars: 14 votes
3 stars: 90 votes
4 stars: 280 votes
5 stars: 408 votes

Frequently Asked Questions

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