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Time-series Forecasting (54 results) showing 1 - 20
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Making cloud savings automated, effortless, and risk-free. Start saving in 2 minutes, no code or engineering required. Maximum Savings: Our AI-driven recommendation engine dynamically adjusts resources based on your needs and usage patterns to maximize savings. Built-In Risk Management: We make fo... | |
Making cloud savings automated, effortless, and risk-free. Start saving in 2 minutes, no code or engineering required. Maximum Savings: Our AI-driven recommendation engine dynamically adjusts resources based on your needs and usage patterns to maximize savings. Built-In Risk Management: We make... | |
ByVITech Lab | Ver 1.0
The Product Demand Forecasting Solution is a cloud-native predictive analytics ML model that analyzes multiple data points, including historical sales data, inventory data, and growth projections to generate up to 50% more accurate product demand forecasts. The solution is scalable and... | |
ByHOSSTED | Ver 2.15.0 Starting from $0.14/hr or from $1,000.00/yr (18% savings) for software + AWS usage fees This is a repackaged open-source software product wherein additional charges apply for supplementary services. Fugu is an open-source Python library for creating spiking neural circuits with computational graphs, enabling neural algorithm exploration. What advantages does the Hossted Fugu... | |
ByIBM Data and AI | Ver v1 IBM's Granite TimeSeries TTM, also known as TinyTimeMixer, is a compact pre-trained model for multivariate time-series forecasting, containing less than 1 million parameters. Despite its small size, TTM outperforms several popular benchmarks that require billions of parameters in both zero-shot and... | |
ByMycom As Communications Service Providers (CSPs) embark on rolling out the latest 5G enterprise services, MYCOMs AInsights application is an essential capability for CSPs to deliver services with AI-based insights for high performance. It operates at two levels: 1) feeding normalized network and service... | |
ByLIT.AI | Ver 0.3.5
Starting from $0.12 to $20.32/hr for software + AWS usage fees This comprehensive MLOps suite is designed to enhance and streamline the machine learning lifecycle, from data handling to deployment. Our product offers a robust set of features for the development process, ensuring efficiency, scalability, and security. | |
Sales, Traffic and Conversions - with AI. Gain insight about your customers and products with aitium® sales, traffic and conversion report visualized for a deeper knowledge of your Amazon storefront. Unlock the full potential of your business with the power of B2B analytics! By harnessing... | |
Byfg-research | Ver 1.7
This algorithm performs time series forecasting with Liquid Neural Networks (LNNs). It implements both training and inference from CSV data and supports both CPU and GPU instances. The training and inference Docker images were built by extending the PyTorch 2.1.0 Python 3.10 SageMaker containers.... | |
ByWhiz.AI | Ver 2024.73.1 WhizAI is the first and only augmented consumer platform purpose-built for the Life Science industry. For business and IT leaders seeking to accelerate time to insight through automation and stakeholder empowerment, the WhizAI platform revolutionizes analytics with contextual AI and genuine NLP... | |
ByWhiz.AI | Ver V2024.73 Starting from $28.53 to $28.53/hr for software + AWS usage fees WhizAI is the first and only augmented consumer platform purpose-built for the Life Science industry. For business and IT leaders seeking to accelerate time to insight through automation and stakeholder empowerment, the WhizAI platform revolutionizes analytics with contextual AI and genuine NLP... | |
Byi4cast LLC | Ver 0.4.0
The long-memory dynamic factor model (LMDFM) algorithm is to make (1) analysis of observed multiple (vector) time-series, (2) multi-step forecasts of multivariate (vector) time-series, and (3) multi-step forecasts of multivariate volatility (variance-covariance matrix) of vector time-series. The ... | |
ByMphasis | Ver 3.1 Product Demand Forecasting generates 36 months of forward forecast of the demand using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automat... | |
ByMphasis | Ver 3.2 Operating Expenses Forecasting generates 30 weeks of forward forecast of the operating expenses using historical data. This will help businesses predict and manage their operating expenses more effectively through better working capital management and improved planning for resource allocation. The... | |
ByMphasis | Ver 3.0 Retail Sales Forecasting generates 30 months of forward forecast of retail sales using historical data. This solution helps retailers to predict future sales and thereby optimize their inventory, logistics, warehouse management, production planning, personnel allocation, etc. It uses ensemble ML... | |
DecisionNext Direct is an easy to use application within the DecisionNext platform that enables our users to buy and sell commodities at the best time, with the best formulas and best prices. This tool consistently delivers 10-20x returns in the first year of service. This is an easy to use dashboa... | |
ByMphasis | Ver 3.3 Energy Consumption Forecasting generates 30 months of forward forecast of the consumption using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution perform... | |
ByMphasis | Ver 3.1 InfraGraf Network Traffic Forecasting helps businesses get a future forecast of the network traffic based on historic data. Benefits offered by this solution includes accurate forecast of network traffic which enables better planning for network infrastructure, application scalability and auto... | |
ByMphasis | Ver 3.1 Inventory Forecasting generates 30 months of forward forecast of the Inventory using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated... | |
Byi4cast LLC | Ver 0.1.0
The variational Bayesian filtering factor analysis (VBfFA) algorithm/model is a filter with dimension-reduction, or rank-reduction, to extract a number of ever-evolving unobserved common factors, or signals from common sources, underlying and influencing a large number of related time-series.... |