10 Challenging Machine Learning Time Series Forecasting Problems

Machine learning methods have a lot to offer for time series forecasting problems.

A difficulty is that most methods are demonstrated on simple univariate time series forecasting problems.

In this post, you will discover a suite of challenging time series forecasting problems. These are problems where classical linear statistical methods will not be sufficient and where more advanced machine learning methods are required.

If you are looking for challenging time series datasets to practice machine learning techniques, you are in the right place.

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Let’s dive in.

Challenging Machine Learning Time Series Forecasting Problems
Photo by Joao Trindade, some rights reserved.

Overview

We will take a closer look at 10 challenging time series datasets from the competitive data science website Kaggle.com.

Not all datasets are strict time series prediction problems; I have been loose in the definition and also included problems that were a time series before obfuscation or have a clear temporal component.

They are:

• How Much Did It Rain? I and II
• Online Product Sales
• Rossmann Store Sales
• Walmart Recruiting – Store Sales Forecasting
• Acquire Valued Shoppers Challenge
• Melbourne University AES/MathWorks/NIH Seizure Prediction
• AMS 2013-2014 Solar Energy Prediction Contest
• Global Energy Forecasting Competition 2012 – Wind Forecasting
• EMC Data Science Global Hackathon (Air Quality Prediction)
• Grupo Bimbo Inventory Demand

This is not all of the time series datasets hosted on Kaggle.
Did I miss a good one? Let me know in the comments below.

How Much Did It Rain? I and II

Given observations and derived measures from polarimetric radar, the problem is to predict the probability distribution of the hourly total in a rain gage.

The temporal structure (e.g. hour to hour) was removed as part of obfuscating the data, which would have made it an interesting time series problem.

The competition was run twice in the same year with different datasets:

• How Much Did It Rain?
• How Much Did It Rain? II

The second competition was won by Aaron Sim, who used a very large recurrent neural network algorithm.

Blog posts interviewing competition winners are accessible here.

Online Product Sales

Given details of the product and the product launch, the problem is to predict the next 12 months of sales figures.

This is a multi-step forecast, or sequence forecast, without a history of sales from which to extrapolate.

I could not find any good write-ups of top performing solutions. Can you?

Learn more on the competition page.

Rossmann Store Sales

Given historical daily sales for more than one thousands stores, the problem is to predict 6 weeks of daily sales figures for each store.

This provides both an opportunity to explore store-wise multi-step forecasts, as well as the ability to exploit cross-store patterns.

Top results were achieved with careful feature engineering and the use of gradient boosting.

Blog posts interviewing competition winners are accessible here.

Learn more on the competition page.

Walmart Recruiting – Store Sales Forecasting

Given historical weekly sales data for multiple departments in multiple stores, as well as details of promotions, the problem is to predict sales figures for store departments.

This provides both an opportunity to explore department-wise and even store-wise forecasts, as well as the ability to exploit cross-department and cross-store patterns.

Top performers made heavy use of ARIMA models and careful handling of public holidays.

See a write-up of the winning solution here, and second place solution here.

Learn more on the competition page.

Acquire Valued Shoppers Challenge

Given historical shopping behavior, the problem is to predict which customers will likely repeat purchase (become acquired) after taking up a discount offer.

The large number of transactions make this a big data download, nearly 3 gigabytes.

The problem provides an opportunity to model the time series of specific or aggregated customers and predict the probability of customer conversion.

I could not find any good write-ups of top performing solutions. Can you?

Learn more on the competition page.

Melbourne University AES/MathWorks/NIH Seizure Prediction

Given a trace of human brain activity observed with an intracranial EEG for months or years, the problem is to predict whether 10-minute segments indicate the probability of a seizure or not.

A 4th place solution is described that made use of statistical feature engineering and gradient boosting.

Learn more on the competition page.

Update: The dataset has since been taken down.

AMS 2013-2014 Solar Energy Prediction Contest

Given historical meteorological forecasts at multiple sites, the problem is to predict the total daily solar energy at each site for one year.

The dataset provides an opportunity to model spatial and temporal time series by site and across sites and make multi-step forecasts for each site.

The winning approach used an ensemble of gradient boosting models.

Learn more on the competition page.

Global Energy Forecasting Competition 2012 – Wind Forecasting

Given historical wind forecasts and power generation at multiple sites, the problem is to predict hourly power generation for the next 48 hours.

The dataset provides an opportunity to model the hourly time series for individual sites as well as across-sites.

I could not find any good write-ups of top performing solutions. Can you?

Learn more on the competition page.

EMC Data Science Global Hackathon (Air Quality Prediction)

Given eight days of hourly measurements of air pollutants, the problem is to forecast pollutants at specific times over the following three days.

The dataset provides an opportunity to model a multivariate time series and perform a multi-step forecast.

A good write-up of the top performing solution describes the use of an ensemble of random forest models trained on lagged variables.

Learn more on the competition page.

Summary

In this post, you discovered a suite of challenging time series forecasting problems.

These are problems that provided the foundation for competitive machine learning on the site Kaggle.com. As such, each problem also provides a great source of discussion and existing world-class solutions that can be used as inspiration and a starting point.

If you are interested in better understanding the role of machine learning for time series forecasting, I would recommend selecting one or more of these problems as a starting point.

Have you worked on one or more of these problems?
Share your experiences in the comments below.

Is there a time series problem on Kaggle.com that was not mentioned in this post?
Let me know about it in the comments below.

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