Multi-horizon Quantile Time Series Forecasting Model

We are happy to announce our new deep learning multi-horizon time series forecasting model, which is part of our Decision Support Platform is a sales forecasting and recommendation platform that helps companies forecast sales and revenues, and suggests a couple of approaches increase your sales. Business owners can also track and understand customers’ churn and improve customers’ retention, along with offering possible key-actions for specific products sold by the company.

This is a non-technical post where we announce the model and we will submit another technical post that explains the model in detail in the coming days.

The model we have developed outperforms Facebook Prophet model by 73% and Amazon DeepAR by 46% using a normalised quantile loss. Below is a comparison conducted on a dataset for an e-commerce retailer in the UK to forecast daily revenue for the next month.

The above shows how our model visually outperforms other models mainly on how accurate the fit is and the ability to follow the spikes in the series. Below is a combined forecasting plot for all models to easily compare each model’s errors

Below is a Line plot for the whole time-series dataset used in the experiment where the hold-out set (Test set) starts at the beginning of November.
The results of median prediction evaluation show that our model outperforms Prophet by 73% and DeepAR by 46% using a normalised quantile loss.

The model is part of our effort to build a Sales recommendation system named “”. You can visit to know more and sign up for the beta version.

AI-Infused Decision Making

Have you ever contemplated in the number of times you make decisions in a given day? It’s time consuming as well as irritating to be reluctant to purchase an item or make a request. What if you have been given a “magical” tool that could help narrow down your choices? What if we create simple tools that can adopt to your style, situation and the current context to give you more precise and accurate recommendations.

If this resonates with you then continue reading this article, as I will share some thoughts around how new type of digital systems can help you and other organisations make better decisions. 

What is AI-Infused Decision Making 

Perhaps I’m endeavoring to coin a new term in the world of the business decision making and artificial intelligence. Working for so many years in the IT field and observing numerous successful and indeed as well as failed projects, one prevalent mistake I see is when organisations think the use of AI can solve any problem and will wondrously help them sell more and market better! well, the conspicuous answer is in the negative! I shall explain.

But first let me give a definition for what do I mean by “AI-Infused Decision Making”. For so many years, people have talked about Expert Systems and how they help make better decisions. According to wikipedia, in artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. The expert system is fed a set of knowledge base with the aim to solve complex problems by applying rule based systems. AI Decision Support systems on the other hand are systems that aid in making decisions. Normally, they provide means for users to interact and use the system to reason and finally conclude.

So what is AI-Infused Decision Making? it’s simply put a combination of both Expert Systems and the use of advanced sophisticated methods in decision support systems to help guide users reach possible decision choices. 

Now that we have defined the term, let us dive into why I believe it provides distinct view and perspective of how new Decision Support Platforms are made and how it contributes into transforming how organisations make strategic and operational decisions. Not just that, even on the personal level, many of us make decisions with the help of mobile or wearable devices. Hence, how significant making decision is! 

Successful Transformation = Taking the Right Decisions on the Right Time 

I would presume many would to concur that making a successful transformation is profoundly correlated with taking the right decisions on the right time. Numerous groundbreaking transformative (ideas, projects, products, etc.) were not completely novel or original but rather composed and packaged entirely in a manner that is truly exhibit solving real and challenging business problems.

So take a moment and think for a second. How many of us when he/she sees a successful business; say I thought about this many years ago? is not that right? well, what went wrong? why didn’t he or she started that business? Was it a lack of will? Perhaps, but I would reckon it’s to do with “Uncertainty“. Our fears of uncertainty heavily influence our decisions. You, for example, made a decision to read so far! if you were uncertain and skeptical about the value of reading this article you would not have spent time thus far reading.

Dealing with Uncertainty? 

We live in a world that is for the most part uncertain, nevertheless we thrive to seek certainty, because certainty gives us confidence in the validity and effectiveness of our decisions. Consequently, most of the organisations look for visionary and talent people who can see through future and anticipate opportunities and make “The Right Decision On the Right Time”.

For so many years, IT companies have sought to create tools to help professionals better make decisions. From automations to the most advanced technologies and use of Machine Learning and to help solve complex problems and provide better recommendations.

A Proposed Model to Solve Uncertainty 

Let us take a logical example of how most people would go about making a decision. Let’s say you want to buy a car. First you will go to the web and search for the car spec, features, reviews, pros and cons, …etc. Then you may contact a subject matter expert for consultation or a friend who happens to own one. Then finally, you look at the available data like cars sales, parts costs ..etc. Isn’t that a very logical approach?

This is precisely what I’m proposing here is to build an AI-Infused system that takes into consideration the previously described approach. The proposed platform will utilise the leading-data found on the Internet and source all reported news and social media feeds, including customer reviews and ratings. This measure represents the uncertainly observed on the social media platform likely to represents people’s views. Incorporate that with Subject Matter Experts Opinion using methods such as Delphi method to source feedback. After that, combine it with lagging-data such as company’s sales, past deals ..etc. The common methods of forecasting depend on choosing internal factors that are often available to the companies or service providers, such as prices, daily sales, etc. However, this method relies on combining the previously mentioned method with inputs from open data such as people’s sentiments about a product or the popularity of a product or company. As well as integrating economic indicators in the forecasting process and enhancing it with the participation of experts such as SMEs. Integrating all of these inputs into a deep learning-based system in an effort to give a more accurate prediction and forecasting than the currently available techniques.

To read and know more about this approach click here

* The images in this article are licensed under Envato Elements