Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent. Although some hierarchical forecasting methods like minimum trace are strongly supported both theoretically and empirically for reconciling the base forecasts, there are still circumstances under which they might not produce the most accurate results, being outperformed by other methods. In this paper we propose an approach for dynamically selecting the most appropriate hierarchical forecasting method and succeeding better forecasting accuracy along with coherence. The approach, to be called conditional hierarchical forecasting, is based on Machine Learning classification methods and uses time series features as leading indicators for performing the selection for each hierarchy examined considering a variety of alternatives. Our results suggest that conditional hierarchical forecasting leads to significantly more accurate forecasts than standard approaches, especially at lower hierarchical levels.

These types of workspaces are rich with data and contain sets of knowledge and information that can be a great source of truth to answer organizational questions. Unfortunately, it isn't always easy to tap into these data sources to extract the information you need. For example, the data source might not be connected to an enterprise search service within the organization, or the service is outdated and lacks natural language search capabilities, leading to poorer search experiences. Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Ken dra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they're looking for, even when it's scattered across multiple locations and content repositories within your organization.

Artificial Intelligence in Retail Market Research Report is a Proficient and In-Depth Study on the Existing State of Artificial Intelligence in Retail Industry.

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

Purchases you make through our links may earn us a commission. If you don't own a robot vacuum by now, chances are, it's an item that's pretty high up there on your Christmas wish list. But spending hundreds of dollars on a quality model may not be something that's in everyone's holiday budget. If you still long for the convenience and ease of an automated vac, we've got the best news for you: The eufy 11S slim robot vacuum--our best-value pick for robot vacuums in 2020--is currently on sale for less than $200 at Amazon. You'll want to note that it won't ship until Sunday, November 1, however, with free delivery between November 4 - 6 for Prime members.

With so many cloud applications infused with artificial intelligence (AI) and machine learning (ML) capabilities, AI/ML is being democratized by cloud services. The growth of AI in a wide range of applications demands more purpose-built processors to provide scalable levels of performance, flexibility, and efficiency. The LG AIoT board helps customers accelerate their computer vision and ML journey using Amazon Web Services (AWS). OEMs can now easily incorporate visual intelligence, voice intelligence, and control intelligence into their products. The LG Neural Engine (LNE) in the LG AIoT board offloads the compute requirements of deep learning algorithms to the specially designed processor, which delivers 1 TFLOPS of compute performance.

This post is courtesy of Eitan Sela, Senior Startup Solutions Architect. Many customers want to deploy machine learning models for real-time inference, and pay only for what they use. Using Amazon EC2 instances for real-time inference may not be cost effective to support sporadic inference requests throughout the day. AWS Lambda is a serverless compute service with pay-per-use billing. However, ML frameworks like XGBoost are too large to fit into the 250 MB application artifact size limit, or the 512 MB /tmp space limit.

The global artificial intelligence (ai) in retail market is expected to rise with an impressive CAGR and generate the highest revenue by 2026. Fortune Business Insights in its latest report published this information. The report is titled "Artificial Intelligence (AI) in Retail Market Size, Share & Industry Analysis, By Offering (Solutions, Services), By Function (Operations-Focused, Customer-Facing), By Technology (Computer Vision, Machine Learning, Natural Language Processing, and Others), and Regional Forecast, 2019-2026". It also offers an exclusive insight into various details such as revenues, market share, strategies, growth rate, product & their pricing by region/country for all major companies. The report provides a 360-degree overview of the market, listing various factors restricting, propelling, and obstructing the market in the forecast duration.

Artificial Intelligence (AI) is considered as the most promising technological advancement in the world of business. AI in retail promises benefits such as enhanced planning, higher scalability, and automated processes along with reduced errors. AI technology continues to transform with new systems that are designed and optimized for specific industries. The retail industry still has a long way to get benefitted from the new tool, mostly because AI software becomes expensive to purchase, integrate, and maintain. Lately, there are very few retail establishments that can justify the expense of integrating an AI system.

Japanese startups are getting ready to deploy a small army of remote-controlled robots in the workplace. Called avatar robots, the machines are still experimental and their initial objectives limited. But if everything goes as planned, they could soon be clerking at convenience stores, patrolling buildings as security guards, or even assisting astronauts in outer space. The technology has the potential to replace humans, helping solve labor shortages and providing relief to essential workers combating natural disasters. Convenience stores in Tokyo have already put prototypes of the robots to work stocking shelves with beverages, instant noodles and other goods.