Best AI tools for< Forecasting Time Series >

The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.

functime is a time-series machine learning tool designed for scalable analysis. It offers a comprehensive set of functions for forecasting, evaluation, and analysis of time-series data. With features like scoring, ranking, and plotting functions, functime simplifies the process of evaluating thousands of forecasts simultaneously. It serves as an AI copilot to help analysts analyze and compare trends, seasonality, and causal factors in forecasts. The tool also provides detailed API reference for seamless integration into existing workflows.

Chaplin is a 100% free AI-based technical analysis tool that offers a wide range of features for stock market analysis. It provides tools for live market analysis, stock, forex, and crypto screening, stock and chart analysis, sentiment analysis, trend prediction, price forecasting, and various strategy simulations. Users can access advanced stock analysis tools, stock scanners, time series analysis, and more. Chaplin also includes tools for portfolio optimization, earnings calendar tracking, financial analysis, investment calculation, and data visualization. The platform aims to assist professional traders with AI-driven insights and tools for making informed investment decisions.

Upmetrics is a business plan software that leverages AI-powered insights to simplify business planning. It offers features such as an AI Business Plan Generator, Financial Forecasting, AI Assistance, AI Pitch Deck Generator, and Strategic Planning. Upmetrics helps entrepreneurs, small businesses, accelerators, consultants, educators, and students in creating actionable business strategies, financial projections, and pitch decks. The platform provides user-friendly interface, exceptional support, and collaborative planning capabilities. With Upmetrics, users can transform their business ideas into actionable plans with real-time forecasting and insightful reporting.

Vindey is an industry-leading AI-powered CRM platform designed for property management and sales solutions. It offers real-time smart suggestions, automation features, and intelligent sales automation to transform customer experiences. Vindey handles the entire sales pipeline, from lead qualification to deal closing, while providing personalized interactions and seamless lead management. The platform also offers AI-powered payment insights, workflow automation, and tailored solutions for various industries, such as sales, healthcare, real estate, education, and manufacturing.

Jyotax.ai is an AI-powered tax solution that revolutionizes tax compliance by simplifying the tax process with advanced AI solutions. It offers comprehensive bookkeeping, payroll processing, worldwide tax returns and filing automation, profit recovery, contract compliance, and financial modeling and budgeting services. The platform ensures accurate reporting, real-time compliance monitoring, global tax solutions, customizable tax tools, and seamless data integration. Jyotax.ai optimizes tax workflows, ensures compliance with precise AI tax calculations, and simplifies global tax operations through innovative AI solutions.

Groundsales.ai is an AI-driven sales forecasting tool that empowers businesses to make accurate predictions and optimize sales strategies. By leveraging advanced analytics and scenario modeling, the platform provides real-time insights and trend analysis to help businesses stay ahead in the competitive market. With seamless data integration and a user-friendly interface, Groundsales.ai offers a data-driven evolution for businesses of all sizes, enabling them to make informed decisions and maximize revenue potential.

Meyka is an AI-powered stock research tool that provides users with real-time stock data and analysis. Users can explore financial health, social sentiment analysis, earnings reports, comparison of financial statements, stock market news, DCF value, stock price forecasting, and recent grades for various stocks. The tool aims to assist users in making informed investment decisions by leveraging AI technology to analyze and predict stock market trends.

Quinyx is an AI-powered Workforce Management, Planning & Scheduling platform that helps businesses optimize their workforce, streamline scheduling, and engage frontline employees. It offers features such as demand forecasting, labor optimization, compliance checking, task management, and communication tools. Quinyx aims to enhance employee productivity, reduce scheduling admin time, and improve overall business performance through AI-driven solutions.

Inventoro is a smart inventory forecasting and replenishment tool that helps businesses optimize their inventory management processes. By analyzing past sales data, the tool predicts future sales, recommends order quantities, reduces inventory size, identifies profitable inventory items, and ensures customer satisfaction by avoiding stockouts. Inventoro offers features such as sales forecasting, product segmentation, replenishment, system integration, and forecast automations. The tool is designed to help businesses decrease inventory, increase revenue, save time, and improve product availability. It is suitable for businesses of all sizes and industries looking to streamline their inventory management operations.

Nextatlas Generate Suite is a cutting-edge AI-powered trend forecasting service that revolutionizes market research and consumer behavior analysis. It offers a range of specialized AI assistants to provide detailed workflow planning, innovation scouting, trend analysis, and brand strategy advice. The suite combines generative AI technology with real-time data analysis to deliver actionable insights and strategic recommendations for businesses across various industries.

PRIYA is an AI tool designed to assist users in pricing strategies. It utilizes artificial intelligence to provide valuable insights and recommendations for optimizing pricing decisions. With PRIYA, users can access advanced pricing analytics and forecasting capabilities to enhance their pricing strategies and maximize profitability. The tool is user-friendly and intuitive, making it suitable for businesses of all sizes looking to improve their pricing strategies.

Drivetrain is a Strategic Finance Platform designed for modern businesses. It offers real-time tracking and reporting, continuous planning and forecasting, and a single source of truth by combining accounting and business data effortlessly. The platform empowers finance teams globally with AI-powered FP&A software, enabling users to accelerate planning, tracking, and forecasting. Drivetrain provides integrations with ERP, CRM, HRIS, and other systems, along with over 200 pre-built connectors. The platform is praised for its collaborative features, user-friendly interface, and ability to make data-driven decisions quickly.

Datarails is a financial planning and analysis platform for Excel users. It automates data consolidation, reporting, and planning while enabling finance teams to continue using their spreadsheets and financial models. With Datarails, finance teams can save time on repetitive tasks and focus on strategic insights that drive business growth.

ACHIV is an AI tool for ideas validation and market research. It helps businesses make informed decisions based on real market needs by providing data-driven insights. The tool streamlines the market validation process, allowing quick adaptation and refinement of product development strategies. ACHIV offers a revolutionary approach to data collection and preprocessing, along with proprietary AI models for smart analysis and predictive forecasting. It is designed to assist entrepreneurs in understanding market gaps, exploring competitors, and enhancing investment decisions with real-time data.

Julius AI is an advanced AI data analyst tool that allows users to analyze data with computational AI, chat with files to get expert-level insights, create sleek data visualizations, perform modeling and predictive forecasting, solve math, physics, and chemistry problems, generate polished analyses and summaries, save time by automating data work, and unlock statistical modeling without complexity. It offers features like generating visualizations, asking data questions, effortless cleaning, instant data export, creating animations, and supercharging data analysis. Julius AI is loved by over 1,200,000 users worldwide and is designed to help knowledge workers make the most out of their data.

ToolsGroup is an AI-powered supply chain and retail planning application that helps retailers, distributors, and manufacturers enhance the resilience and performance of their operations. Leveraging AI and real-time data, ToolsGroup offers probabilistic planning solutions for demand forecasting, inventory optimization, replenishment, assortment planning, and more. The application automates manual tasks, guides smarter decisions with AI, and delivers outstanding customer experiences with real-time visibility across the enterprise.

Business Machine is an AI-powered tool designed to assist in business planning. It utilizes advanced algorithms and machine learning to analyze data, trends, and market insights to provide valuable recommendations for strategic decision-making. With Business Machine, users can create detailed business plans, forecast financial outcomes, and optimize their operations for growth and success.

TimeHero is an AI-powered productivity tool that offers smart task planning and work management solutions for teams and individuals. It helps users schedule, manage, and automate daily tasks, projects, and calendar events in one centralized platform. TimeHero stands out by automatically planning when to work on tasks based on availability, adjusting plans instantly when events change, tasks are completed early, or priorities shift. With features like adaptive planning, autonomous recurring tasks, smart workflow templates, built-in time tracking, automatic risk detection, and project forecasting, TimeHero streamlines work processes and enhances productivity for remote and in-office teams alike.

Rainbow Weather is an AI-powered weather forecasting application that provides super accurate rain forecasts, local radar information, and hurricane tracking. The app aims to help users plan their day effectively by delivering precise weather updates in real-time. With the use of AI technology, Rainbow Weather ensures that users stay informed and safe by notifying them about weather changes for their exact location. The app is designed to keep users ahead of the weather and prevent them from getting caught in unexpected rain showers. Rainbow Weather's mission is to deliver the most reliable weather information to users, enabling them to make informed decisions and stay safe.

PyTorch Forecasting is a PyTorch-based package for time series forecasting with state-of-the-art network architectures. It offers a high-level API for training networks on pandas data frames and utilizes PyTorch Lightning for scalable training on GPUs and CPUs. The package aims to simplify time series forecasting with neural networks by providing a flexible API for professionals and default settings for beginners. It includes a timeseries dataset class, base model class, multiple neural network architectures, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. PyTorch Forecasting is built on pytorch-lightning for easy training on various hardware configurations.

LLMsForTimeSeries is a repository that questions the usefulness of language models in time series forecasting. The work shows that simple baselines outperform most language model-based time series forecasting models. It includes ablation studies on LLM-based TSF methods and introduces the PAttn method, showcasing the performance of patching and attention structures in forecasting. The repository provides datasets, setup instructions, and scripts for running ablations on different datasets.

sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.

LLMs4TS is a repository focused on the application of cutting-edge AI technologies for time-series analysis. It covers advanced topics such as self-supervised learning, Graph Neural Networks for Time Series, Large Language Models for Time Series, Diffusion models, Mixture-of-Experts architectures, and Mamba models. The resources in this repository span various domains like healthcare, finance, and traffic, offering tutorials, courses, and workshops from prestigious conferences. Whether you're a professional, data scientist, or researcher, the tools and techniques in this repository can enhance your time-series data analysis capabilities.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

Time-LLM is a reprogramming framework that repurposes large language models (LLMs) for time series forecasting. It allows users to treat time series analysis as a 'language task' and effectively leverage pre-trained LLMs for forecasting. The framework involves reprogramming time series data into text representations and providing declarative prompts to guide the LLM reasoning process. Time-LLM supports various backbone models such as Llama-7B, GPT-2, and BERT, offering flexibility in model selection. The tool provides a general framework for repurposing language models for time series forecasting tasks.

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

CALF (LLaTA) is a cross-modal fine-tuning framework that bridges the distribution discrepancy between temporal data and the textual nature of LLMs. It introduces three cross-modal fine-tuning techniques: Cross-Modal Match Module, Feature Regularization Loss, and Output Consistency Loss. The framework aligns time series and textual inputs, ensures effective weight updates, and maintains consistent semantic context for time series data. CALF provides scripts for long-term and short-term forecasting, requires Python 3.9, and utilizes word token embeddings for model training.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

Transformers_And_LLM_Are_What_You_Dont_Need is a repository that explores the limitations of transformers in time series forecasting. It contains a collection of papers, articles, and theses discussing the effectiveness of transformers and LLMs in this domain. The repository aims to provide insights into why transformers may not be the best choice for time series forecasting tasks.

LLM-PlayLab is a repository containing various projects related to LLM (Large Language Models) fine-tuning, generative AI, time-series forecasting, and crash courses. It includes projects for text generation, sentiment analysis, data analysis, chat assistants, image captioning, and more. The repository offers a wide range of tools and resources for exploring and implementing advanced AI techniques.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

Awesome-TimeSeries-SpatioTemporal-LM-LLM is a curated list of Large (Language) Models and Foundation Models for Temporal Data, including Time Series, Spatio-temporal, and Event Data. The repository aims to summarize recent advances in Large Models and Foundation Models for Time Series and Spatio-Temporal Data with resources such as papers, code, and data. It covers various applications like General Time Series Analysis, Transportation, Finance, Healthcare, Event Analysis, Climate, Video Data, and more. The repository also includes related resources, surveys, and papers on Large Language Models, Foundation Models, and their applications in AIOps.

PyTorch Forecasting is a PyTorch-based package designed for state-of-the-art timeseries forecasting using deep learning architectures. It offers a high-level API and leverages PyTorch Lightning for efficient training on GPU or CPU with automatic logging. The package aims to simplify timeseries forecasting tasks by providing a flexible API for professionals and user-friendly defaults for beginners. It includes features such as a timeseries dataset class for handling data transformations, missing values, and subsampling, various neural network architectures optimized for real-world deployment, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. Built on pytorch-lightning, it supports training on CPUs, single GPUs, and multiple GPUs out-of-the-box.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

Awesome Large Weather Models (LWMs) is a curated collection of articles and resources related to large weather models used in AI for Earth and AI for Science. It includes information on various cutting-edge weather forecasting models, benchmark datasets, and research papers. The repository serves as a hub for researchers and enthusiasts to explore the latest advancements in weather modeling and forecasting.

Burr is a Python library and UI that makes it easy to develop applications that make decisions based on state (chatbots, agents, simulations, etc...). Burr includes a UI that can track/monitor those decisions in real time.

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