Time is Money! Automate Your Time-Series Forecasts with Driverless AI

Time-series forecasting is one of the most common and important tasks in business analytics. There are many real-world applications like sales, weather, stock market, energy demand, just to name a few. We strongly believe that automation can help our users deliver business value in a timely manner. Therefore, once again we translated our Kaggle Grand Masters’ time-series recipes into our automatic machine learning platform Driverless AI (version 1.2). This blog post introduces the new time-series functionality with a simple sales forecasting example.

The key features/recipes that make automation possible are:

  • Automatic handling of time groups (e.g. different stores and departments)
  • Robust time-series validation
            – Accounts for gaps and forecast horizon
            – Uses past information only (i.e. no data leakage)
  • Time-series specific feature engineering recipes
            – Date features like day of week, day of month etc.
            – AutoRegressive features like optimal lag and lag-features interaction
            – Different types of exponentially weighted moving averages
            – Aggregation of past information (different time groups and time intervals)
            – Target transformations and differentiation
  • Integration with existing feature engineering functions (recipes and optimization)
  • Automatic pipelines generation (see this blog post)

A Typical Example: Sales Forecasting

Below is a typical example of sales forecasting based on Walmart competition on Kaggle. In order to frame it as a machine learning problem, we formulate the historical sales data and additional attributes as shown below:

Raw data:

Data formulated for machine learning:

Once you have your data prepared in tabular format (see raw data above), Driverless AI can formulate it for machine learning and sort out the rest. If this is your very first session, the Driverless AI assistant (new feature in version 1.2) will guide you through the journey.

Similar to previous Driverless AI examples, users need to select the dataset for training/test and define the target. For time-series, users need to define the time column (by choosing AUTO or selecting the date column manually). If weighted scoring is required (like the Walmart Kaggle competition), users can select the column with specific weights for different samples.

If users prefer to use automatic handling of time groups, they can leave the setting for time groups columns as AUTO.

Expert users can define specific time groups and change other settings as shown below.

Once the experiment is finished, users can make new predictions and download the scoring pipeline just like any other Driverless AI experiments.

Seeing is believing. Try Driverless AI yourself today. Sign up here for a free 21-day trial license.

Until next time,
Joe

Bonus fact: The masterminds behind our time-series recipes are Marios Michailidis and Mathias Müller so internally we call this feature AutoM&M.

AI in Healthcare – Redefining Patient & Physician Experiences

Register for the Meetup Here

Patients, physicians, nurses, health administrators and policymakers are beneficiaries of the rapid transformations in health and life sciences. These transformations are being driven by new discoveries (etiology, therapies, and drugs/implants), market reconfiguration and consolidation, a movement to value-based care, and access/affordability considerations. The people and systems that are driving these changes are generating new engagement models, workflows, data, and most importantly, new needs for all participants in the care continuum.

Analytics 1.0 (driven by business intelligence & reporting) for Healthcare as we describe in our book is inadequate to address these transformations. A retrospective understanding of “what happened?” is limited in its usefulness as it only provides for corrective action – usually driven by resource availability. To improve wellness, care outcomes, clinician satisfaction, and patient quality of life, we ought to be leveraging little and big data via Analytics 2.0 & 3.0. This journey will require leveraging machine/deep learning and other AI methods to separate signal from noise, integrate insights into a workflow, address data fidelity, and develop contextually-intelligent agents.

Automating machine learning and deep learning simplifies access to these advanced technologies by the Humans of Healthcare. They are key pre-requisites to create a data-driven, learning Healthcare organization. The net results – better science, improved access & affordability, and evidence-based wellness/care.

Among others involved in the care continuum, physicians are at the forefront of the coming health sciences revolution. Join our expert, all-physician panel at the H2O offices in Mountain View, CA to hear their expert thoughts and interact with them. Our panel consists of 3 leading physician leaders who are also driving clinical innovations using AI in their specialties & organizations:

  1. Dr. Baber Ghauri, Physician Executive and Healthcare Innovator, Trinity Health

  2. Dr. Esther Yu, Professor & Neuroradiologist, UCSF

  3. Dr. Pratik Mukherjee, Professor, and Director of CIND, San Francisco VA

  4. Moderator: Prashant Natarajan, Sr. Dir. AI Apps at H2O.ai and best-selling author/contributor to books on medical informatics & analytics

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p>We look forward to seeing you in person.

-H2O.ai Team

H2O World coming to NYC

H2O World coming to NYC

Whether you’re just starting out learning how machine learning and H2O.ai can supercharge your business or a veteran looking for more, we want to invite you to join some of greatest minds in the field to learn how AI and H2O.ai can transform your business. Our flagship event, H2O World is back and it’s going to be bigger than ever! We’re making our way around the world with our first stop at The New York Academy of Sciences on June 7th.

You’ll get exclusive access to the brains behind the operations of open source, H2O, H2O Driverless AI, Sparkling Water, MLI, and more! You’ll even be able to get a hands-on tutorial of our revolutionary Driverless AI platform and learn directly from the people implementing H2O.ai’s solutions to solve some of their companies’’ toughest problems.

With an eclectic group of speakers of product managers, data scientists, customer success managers, and more, we’ve got something for everyone! Don’t miss out on a full day of talks and hands-on sessions. Learn how H2O.ai is democratizing machine learning and transforming businesses in all industries ranging from healthcare, finance, insurance and more.

Highlights from Last year:

Leah Liebler
Marketing @ H2O