Applied Time Series Analysis in Python
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
Instructor
- 4.5 Instructor Rating
- 182 Reviews
- 1,030 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.