Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Introduction to Time-series Forecasting	How can we predict future values in a time-series?
Duration: 00h 30m	2. Baseline Metrics for Timeseries Forecasts	What are some common baseline metrics for time-series forecasting?
Duration: 01h 20m	3. Moving Average Forecasts	How can we analyze time-series data with trends?
Duration: 02h 10m	4. Autoregressive Forecasts	How can we enhance our models to account for autoregression?
Duration: 03h 00m	5. Autoregressive Moving Average Forecasts	How can we forecast time-series with both moving average and autoregressive processes?
Duration: 03h 50m	6. Autoregressive Integrated Moving Average Forecasts	How can we forecast non-stationary time-series?
Duration: 04h 40m	7. Seasonal Autoregressive Integrated Moving Average Forecasts	How do we account for seasonal processes in time-series forecasting? ::::::::::::::::::::::::::::::::::::::::::::::::
Duration: 05h 30m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

The source dataset this used for lesson consists of smart meter power consumption data provided by the Los Alamos Public Utility Department (LADPU) in Los Alamos, New Mexico, USA. In their original format the data have only been processed to remove consumer information. The data contain missing and duplicate values.

The original dataset on which the lesson materials are based is available from Dyrad, LADPU Smart Meter Data, https://doi.org/10.5061/dryad.m0cfxpp2c and has been made available with a CC-0 license:

Souza, Vinicius; Estrada, Trilce; Bashir, Adnan; Mueen, Abdullah (2020), LADPU Smart Meter Data, Dryad, Dataset, https://doi.org/10.5061/dryad.m0cfxpp2c

Data Sets

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For this lesson, the data have been modified to support the lesson objectives without requiring a download of the full source dataset from Dryad. Because the source data are large and require cleaning, additional steps have been taken to generate a subset ready for use in this lesson. These steps include:

• Excluding data from meters that were not participating for the full period between January 1, 2014 and December 31, 2019.
• Excluding data from meters that have missing or duplicate readings, or other anomalies.
• Further limiting the included date ranges to exclude common outliers across datasets due to weather events, power outages, or other causes.
• Selection of the final set of 15 data files based on inspection of plots and completeness of the data.

At the outset of a lesson, learners are recommended to create a project directory.

1. Download this data file to your computer: Smart meter data subset
2. Within a directory on their system for which learners have read and write permissions (user home, desktop, or similar), create a directory named pandas_timeseries.
3. In the pandas_timeseries directory, create a subdirectory named data. Unzip the downloaded data into this directory.
4. In the pandas_timeseries directory, create two more directories, scripts and figures.

Throughout the lesson, we will be creating scripts in the scripts directory. If using Jupyter Notebooks, be sure to navigate to this directory before creating new notebooks!

Software Setup

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Details

The lesson is written in Python. We recommend the Anaconda Python distribution, which is available for all operating systems and comes with most of the necessary libraries installed. Information on how to download and install for different operating systems is available from the Anaconda website.

There are different options for running a Python environment.

Using Jupyter Notebook

The Anaconda distribution recommended above includes Jupyter Notebook, which is a browser-based electronic notebook environment that supports Python, R, and other languages.There are two ways that you can launch a notebook server. The first option is to run the application from the Anaconda Navigator:

1. Launch Anaconda Navigator using your operating system’s application launcher.
2. The Navigator is a utility for managing environments, libraries, and applications. Find the Jupyter Notebook application and click on the Launch button to start a notebook server:
3. The Jupyter Notebook server will open up a file navigator in your home directory of your operating system. Click through to navigate to the project scripts directory created in the setup, above. Click on New and select Python 3 to create a Jupyter Notebook in that directory.
4. A new “Untitled” notebook will open up. When you see an empty notebook cell you are ready to go!

A second option is to use a command line client.

1. Open the default command line utility for your operating system. For Mac and many Linux systems, this will be the Terminal app. On Windows, it is recommended to launch either the CMD.exe Prompt or the Powershell Prompt from the Navigator.
2. Use the cd or change directory command to navigate to the scripts subdirectory of the project directory created in the setup section above.

cd ~/Desktop/pandas_timeseries/scripts

3. Launch a Jupyter Notebook server using the jupyter notebook command. When the server launches, information similar to the below will appear in the console:

4. The Jupyter Notebook application will also open in a web browser. Click on New and select Python 3 to create a Jupyter Notebook in that directory.

5. A new “Untitled” notebook will open up. When you see an empty notebook cell you are ready to go!

6. When you are finished working, after closing the Jupyter browser interface, be sure to also stop the server using CONTROL-C.

Using a command line client

1. Open a shell and enter python3