Workshop material for Time Series Analysis in Python by Amit Kapoor and Bargava Subramanian
Experience Level : Beginner
Overview: A lot of data that we see in nature are in continuous time series. This workshop will provide an overview on how to do time series analysis and introduce time series forecasting.
Audience: People interested in Data analytics on time series data.
Objective:
- What is time series data?
- How to visualize time series data
- How to analyze time series data ?
- How to forecast time series data?
Weather data, stock prices, population of a country are all examples of time series data. The data is continuously recorded daily, weekly, monthly etc. While a lot of theory has been developed for representing and analyzing data at a point in time, many of those don't work well with continuous time series data.
The goal of this workshop is two-fold:
- How to analyze/visualize time-series data
- How to forecast using the available time-series data
We will take a principled scientific approach on how to gather data, prepare data and explore it. We will create some summary metrics using the available data.
Then we will define the problem(s) we want to forecast and introduce some of the common time series forecasting models and implement them using Python.
Outline
- Obtaining time series data
- Determine what questions need to be answered
- Generate hypotheses for various solution approaches
- Exploring time series data
- Outliers
- Missing values
- Creating aggregate metrics
- Calculate percentage/proportion metrics
- Summary metrics
- Visualize time series data
- Time Series forecasting
- Linear regression
- Moving average
- Time series decomposition
- ARIMA
- Dynamic Regression Models
- Vector Autoregression
- Exponential Smoothing
Script to check if requisite libraries for the workshop is present Please execute the following command at the command prompt
$ python check_env.py
If any library has a FAIL message, please install/upgrade that library.
Installation instructions can be found here
Time Series Analysis using Python by Amit Kapoor and Bargava Subramanian is licensed under a MIT License.