Generative AI has since been applied in various fields, including knowledge management (KM), chatbots, meeting recording, and coding. Recent international academic research has also utilized generative AI for time-series data prediction across multiple task topics, and even combined the prediction results of generative AI time-series data with external databases or textual knowledge, further utilizing large language models for deeper and broader insightful analysis – LLM BI Knowledge Applications.
In this era of data explosion, predictive analytics has become an indispensable tool across all industries. By uncovering valuable information hidden within historical data, businesses can predict critical issues such as customer churn and equipment failures, and take corresponding countermeasures to significantly improve operational efficiency. However, traditional predictive analytics technologies face numerous challenges, such as: complex model construction processes, extensive experimentation in algorithm selection and model training, excessive data dimensions, diverse data formats and sources, and a shortage of specialized talent. This presents an exciting application prospect for emerging technologies like Predictive GenAI (Predictive Generative Artificial Intelligence).
What is Predictive GenAI? Predictive GenAI combines Predictive AI and Generative AI, leveraging the key strengths of both. It uses machine learning algorithms to extract information from training data and makes predictions about new data based on these analyses. GenAI, on the other hand, refers to large-scale language models (LLMs) trained on massive amounts of text, possessing billions to hundreds of billions of model parameters and an astonishing ability to understand and generate human language. Predictive GenAI completely solves many of the challenges faced by traditional predictive analytics, such as predicting time series data. Time series data refers to data sequences arranged in chronological order. In time series data analysis, the collected data is arranged according to the order of points in time. Since time series data is usually collected at regular time intervals, the accumulation rate of data is often quite astonishing, such as telemetry data collected by IoT sensors in smart factories.
When processing time-series data, the volume of data is indeed a crucial consideration. Once data accumulates over several years or even decades, handling high-dimensional, multivariate, and diverse data formats and sources presents a significant challenge for traditional machine learning methods. Predictive GenAI, through its generative AI capabilities, can automatically identify patterns and rules from massive amounts of data and quickly utilize these patterns and rules to predict future events or generate new content, assisting us in accomplishing more complex tasks. By fine-tuning the Time Series model using all historical data according to the dialogue format of a large language model, and leveraging the high-speed computing power of AIHPC currently available, model training on historical data of tens to hundreds of millions of tokens can be completed in just 3-5 hours. Afterward, users can directly ask questions about Time Series predictions via ChatBot dialogue.
Furthermore, Predictive GenAI differs from traditional rule-based machine learning. Traditional AI primarily employs rule-based programming, requiring manual setting of rules and logic. Therefore, building an accurate predictive model in the past often necessitated extensive involvement from data analysts and even data scientists, demanding significant human and financial resources from businesses. Now, users simply interact with Predictive GenAI, describing the business challenge to be predicted in natural language. Predictive GenAI can then autonomously learn, understand, acquire knowledge, and automatically build a suitable predictive model. Coupled with a no-code platform, no coding is required, allowing anyone to easily complete predictive modeling tasks.
In practice, Taizhiyun used the FFM-Llama2-7B language model to fine-tune the Predictive GenAI for preventative warranty prediction (mean absolute error (MAE) = 2.7), and in the experiment of predicting the rise and fall range of S&P 500 index stock prices, it created a Predictive GenAI for financial stock price prediction (mean absolute error (MAE) = 1.41~2.78). In both cases, the process from data collection to fine-tuning training was completely without the participation of data scientists. The model building was completed in just a few hours and quickly, and good prediction results were obtained.
While Predictive GenAI has opened a new door for predictive analytics, generative AI technology is still developing rapidly. This technology still faces interpretability issues. Due to the "black box" nature of the model's internal mechanisms, users may find it difficult to understand the logic behind certain predictions. We still need to continuously monitor data security and potential, little-known risks. Taizhiyun's experiment using Predictive GenAI in a prediction-themed setting is a successful attempt. This innovative technology also brings new thinking and methods to decision-makers and related technical personnel. We believe that by properly utilizing Predictive GenAI's powerful insights, it can empower businesses with intelligent decision-making, achieve brand goals, and create better operational efficiency and business opportunities.
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