Multimodal Time Series Analysis and Forecasting System
Project scope
Categories
Data analysis Data modelling Machine learning Artificial intelligence Data scienceSkills
ai agents inventory management time series analysis and forecasting ibm system p unstructured data performance appraisal retrieval augmented generation systems architecture financial forecasting forecastingThe project aims to develop a sophisticated AI system that enhances time series forecasting by integrating unstructured data insights. The primary goal is to create an AI agent capable of analyzing both numerical time series data and related textual information to improve prediction accuracy. This involves building a Retrieval-Augmented Generation (RAG) system that can efficiently retrieve relevant time series segments based on natural language queries. Additionally, the project focuses on implementing a multimodal AI system designed for anomaly and trend detection, leveraging both structured and unstructured data. By combining these elements, the project seeks to provide a comprehensive solution for understanding and predicting complex patterns in time series data, which is crucial for various applications such as financial forecasting, inventory management, and more.
The project will deliver a functional AI agent that integrates numerical and textual data for enhanced forecasting. Key deliverables include a working RAG system for retrieving time series segments via natural language queries, and a multimodal AI system capable of detecting anomalies and trends. The final output will be a comprehensive report detailing the system's architecture, implementation, and performance evaluation, along with a user guide for practical application.
Providing specialized, in-depth knowledge and general industry insights for a comprehensive understanding.
Sharing knowledge in specific technical skills, techniques, methodologies required for the project.
Providing access to necessary tools, software, and resources required for project completion.
About the company
Representation
Diversity and inclusion
Categories highlighting this companyβs ownership and values
Minority-Owned BIPOC-Owned 2slgbtqia+-owned Social Enterprise Immigrant-Owned Community-FocusedExecutive Summary of ImmiCan
Overview:
ImmiCan, under the leadership of Saad Khan, founder and CEO, is an innovative startup at the seed stage, focused on facilitating the economic integration of immigrants.
Mission:
ImmiCan is dedicated to accelerating the settlement process for immigrants, providing a streamlined platform where they can receive guidance and access essential services.
Product and Service:
Our product is a AI assistant designed for immigrants to overview a roadmap and connect with businesses. This assistant guides them through the process of settling in a new environment, connecting them with various services offered by businesses on our platform.
Target Market:
ImmiCan targets global immigrants seeking assistance with economic integration and business owners looking to offer their services to this demographic.
Unique Value Proposition:
ImmiCan stands out with its dual-component platform: an AI assistant tailored for immigrants and a comprehensive front-end suite for business owners. This combination addresses the needs of both immigrants and service providers in a single, integrated ecosystem.
Technology:
The technological backbone of ImmiCan includes Front End development in Reach, Back End in Flask, a Postgresql database, and servers currently hosted on AWS/Azure. We are considering a shift to dedicated servers for enhanced performance.
Team:
The core team comprises CEO Saad Khan, Head of IT Yasir Mohammed with over 20 years of IT business experience in Italy,
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