Time Series Modeling: ML and Deep Learning Approaches with Python

Programming review

• pandas
• scikit-learn
• statsmodels
• Keras

Understanding time series

• Empirical examples
• Trends
• Seasons and cycles

Analyzing time series data

• Timesseries transformations (diff, lag, sqrt, etc.)
• Resampling and fill methods
• Bootstrapping and Jacknife
• Autocorrelations and partial autocorrelation function
• Correlations of two time series
• Visualizing time series

Random walks

• White noise
• Drift
• Smoothing and rolling window
• Fast Fourier Transform

Exponential Smoothing Methods

• Basic Exponential Smoothing
• Trends
• Holt-Winters
• Forecasting

ARIMA models

• Auto regressive (AR) models
• Moving averages (MA)
• Fitting ARIMA models
• Seasonal ARIMA models

Code and slides to accompany the online series of webinars: https://data4sci.com/timeseries by Data For Science.

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 to become an ever more important skill in any data scientists toolkit.

In this training, 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.

Slides: https://github.com/DataForScience/Timeseries_long/blob/master/slides/Timeseries_long.pdf