Applied Time Series Analysis in Python

Name: Applied Time Series Analysis in Python
Rating: 4.3 (138 reviews)

Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis

(138 ratings)

690 students

Created by Marco Peixeiro

Last updated 2/2021

English

English [Auto]

What you'll 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

Requirements

Basic knowledge of Python
Basic knowledge of deep learning
Jupyter notebook installed (or access to Google Colab)

Description

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.

Who this course is for:

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

Marco Peixeiro

Data Scientist and Instructor

4.3 Instructor Rating
138 Reviews
690 Students
1 Course

Experience as a data scientist

I completed a bachelor degree in a field that did not interest me. Instead, I started learning web development on the side and landed my first job as a web developer.

I went on to teach myself data science, as I was very curious about the idea of machines learning by themselves. I proceeded to land another job as a professional data scientist, even though I do not have a masters or a PhD.

As a self-taught data scientist and web developer, I know what it feels like to dive in a completely new field. I know the hard parts, and I know what must be taught to land a professional job and gain new skills with a real impact on our career.

Experience as an instructor

As far I can remember, I was always the person explaining to my peers. Through tutoring, blog articles, and courses, I have a passion for sharing my knowledge and teaching. I strive to have an impact on my students and see them become better and more knowledgeable.