Deep Learning from Scratch
Published by Pearson
Data Processing and Modeling
The availability of large quantity of cheap sensors brought forth by the so-called “Internet of Things” has resulted in an explosion of the amounts of time-varying data. Understanding how to mine, process and analyze such data will only become an ever more important skill in any data scientists toolkit.
In this lecture, we will work through the entire process of how to analyze and model time series data, how to detect and extract trend and seasonality effects and how to implement the ARIMA class of forecasting models. Both real and synthetic datasets will be used to illustrate the different kinds of models and their underlying assumptions.
What you’ll learn and how you can apply it
- Analyze and process time varying data
- Identify the different kinds of drifts, lags and trends in time series data
- Understand auto-correlations and partial auto-correlations
- Generate and use random walks as test beds for time series
- Implement a wide range of ARIMA modes with nothing but basic Python
This live event is for you because...
- Work with time-varying data
- Are curious about the generic mechanisms that result in time series
- Want to learn how to handle model time series
- Wish to Identify trends and correlations in time series
- Apply time series analysis to real-world datasets
Prerequisites
Attendees should have experience with:
- Basic Python
- Numpy
- Matplotlib
- Jupyter
Course Set-up
- Scientific Python distribution like Anaconda
Recommended Preparation
- Watch: Learning Data Structures and Algorithms with Rob Stephens
Recommended Follow-up
- Attend: Building Intelligent Analytics through Time Series Data (search for upcoming courses)
- Attend: Time Series Forecasting with Matt Harrison
- Attend: Hands-on Machine Learning with Python: Clustering, Dimension Reduction, and Time Series Analysis with Francesca Lazzeri
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1 – Understanding Time Series (30 minutes)
- Empirical Examples
- Trends
- Seasons and Cycles
Break (10 minutes)
Segment 2 – Processing Time Series Data (60 minutes)
- Time series transformations (diff, lag, sqrt, etc)
- Resampling/fill methods
- Bootstrapping/Jacknife
- Autocorrelations and Partial Autocorrelation Function
- Correlations of 2 time series
Break (10 minutes)
Segment 3 – Random Walks (30 minutes)
- White noise
- Drift
- Smoothing/Rolling window
- Fast-Fourier Transform
Break (10 minutes)
Segment 4 – ARIMA Models (60 minutes)
- Auto-regressive models (AR)
- Moving Averages (MA)
- Fitting ARIMA models
- Seasonal ARIMA models
Your Instructor
Bruno Gonçalves
Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.