While univariate models for representing time series are useful in many applications, jointly modeling multiple time series can increase accuracy of various tasks, such as forecasting, anomaly detection and pattern discovery. There are two main hindrances to applying these models more widely. The first is wide variability of behaviors of different time series, making it harder to capture their different behaviors in a single model. Solutions may be borrowed from other challenges ML researchers face in other data domains: for example, in other machine learning tasks, different scales between features is addressed using normalization techniques. A second challenge is joint modeling of time series that are measured/reported at different time intervals (e.g., reporting every second, minute or hour), and sometimes reported at irregular intervals.

A recent research study on the global Artificial Intelligence in Medical Imaging market presented by Zeal Insider offers a detailed analysis of key market players, market revenue, market segments, share, and geographic regions. It also offers several industry trends and predictions for upcoming Eight years. The report also puts light on the current COVID-19 pandemic situations on the Global Artificial Intelligence in Medical Imaging Market enabling the user to propose strategic growth plans and tactical business judgments. The size of global Artificial Intelligence in Medical Imaging market is estimated to grow during the forecast period of 2020 to 2028 with a CAGR of xx% and is estimated to reach.US$ xx million by 2028, from US$ xx million in 2020. The report explains the degree of COVID-19 impact on every segment under the scope of the report with its trend over the forecast period.

AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market. Some of the leading companies influencing the market are Ag Leader Technology, Trimble, Agribotix, Granular, SAP, Mavrx, PrecisionHawk, aWhere, IBM and Prospera Technologies.

Artificial intelligence in transportation helps the transportation companies to ensure public safety for their service. Artificial Intelligence in transportation makes use of various concepts like deep learning, computer vision, and context awareness to know the way the drivers handle their resources. The global artificial intelligence in the transportation market is experiencing high demand due to the increasing popularity of the autonomous vehicle. Various organizations are using AI in transportation solutions for data collection and decision making. The growing use of autonomous vehicles, and need to control the operational costs are the major factors that are expected to support the growth of artificial intelligence in transportation market whereas failure in performance is the major factor that is expected to slow down the growth of this market. The "Global Artificial intelligence in Transportation Market Analysis to 2027" is a specialized and in-depth study of the artificial intelligence in transportation industry with a focus on the global market trend.

These notions represent the new technologies trends that increase the competitiveness of an e-commerce. By 2016, 9 out of 10 customers globally were using messaging to interact with companies. To remain competitive, e-commerce must adapt to the rapid evolution of digital technologies and the behavior of Internet users. Statistics shows that average time saving per chatbot inquiry when compared with traditional call centers is 4 minutes in chatbots for the banking & healthcare sectors. By 2022 $8 billion in cost savings is expected.