Tag: prediction

Prediction with data

Posted on by lechuck park

This image illustrates a comparison between two approaches for Prediction with Data.

Left Side: Traditional Approach (Setup First Configuration)

The traditional method consists of:

  • Condition: 3D environment and object locations
  • Rules: Complex physics laws
  • Input: 1+ cases
  • Output: 1+ prediction results

This approach relies on pre-established rules and physical laws to make predictions.

Right Side: Modern AI/Machine Learning Approach

The modern method follows these steps:

  1. Huge Data: Massive datasets represented in binary code
  2. Machine Learning: Pattern learning from data
  3. AI Model: Trained artificial intelligence model
  4. Real-Time High Resolution Data: High-quality data streaming in real-time
  5. Prediction Anomaly: Final predictions and anomaly detection

Key Differences

The most significant difference is highlighted by the question “Believe first ??” at the bottom. This represents a fundamental philosophical difference: the traditional approach starts by “believing” in predefined rules, while the AI approach learns patterns from data to make predictions.

Additionally, the AI approach features “Longtime Learning Verification,” indicating continuous model improvement through ongoing learning and validation processes.

The diagram effectively contrasts rule-based prediction systems with data-driven machine learning approaches, showing the evolution from deterministic, physics-based models to adaptive, learning-based AI systems.

With Claude

Prophet

Posted on by lechuck park

With a Claude’s help
The image appears to be a diagram or concept map that explains the components of the Prophet forecasting model, which is a popular time series forecasting library in Python. Here’s a breakdown of the key elements:

The diagram also shows different types of trend, seasonality, and holiday effects that the Prophet model can handle.

The main function is y(t), which represents the time series data that needs to be forecasted.

y(t) is composed of four additive components:

g(t): The trend component, which represents the long-term linear or piecewise linear growth trend in the data.

s(t): The seasonality component, which captures yearly and weekly seasonality patterns in the data.

h(t): The holiday effects component, which accounts for the impact of holidays or special events on the data.

e: The error term, which represents noise and uncertainty in the data.

Time Series Prediction : 3 types

Posted on by lechuck park

with a Claude’s help
This image provides an overview of different time series prediction methods, including their characteristics and applications. The key points are:

ARIMA (Autoregressive Integrated Moving Average):

  • Suitable for linear, stable datasets where interpretability is important
  • Can be used for short-term stock price prediction and monthly energy consumption forecasting

Prophet:

  • A quick and simple forecasting method with clear seasonality and trend
  • Suitable for social media traffic and retail sales predictions

LSTM (Long Short-Term Memory):

  • Suitable for dealing with nonlinear, complex, large-scale, feature-rich datasets
  • Can be used for sensor data anomaly detection, weather forecasting, and long-term financial market prediction

Application in a data center context:

  • ARIMA: Can be used to predict short-term changes in server room temperature and power consumption
  • Prophet: Can be used to forecast daily, weekly, and monthly power usage patterns
  • LSTM: Can be used to analyze complex sensor data patterns and make long-term predictions

Utilizing these prediction models can contribute to energy efficiency improvements and proactive maintenance in data centers. When selecting a prediction method, one should consider the characteristics of the data and the specific forecasting requirements.

Prediction & Detection

Posted on by lechuck park

From Claude with some prompting
This image illustrates a Prediction and Detection system for time series data. Let me break down the key components:

  1. Left Large Box (Learning and Prediction Section):
  • Blue line: Actual Temperature data
  • Red dotted line: Predicted Temperature data
  • Uses time series prediction models like LSTM, ARIMA, and Prophet for learning
  1. Top Right (Threshold-based Anomaly Detection):
  • “Abnormal Detection with Threshold”
  • Detects abnormal temperature changes based on threshold values
  • The area marked with a red circle shows where values exceed the threshold
  • Includes “Warning” and “Critical” threshold levels
  1. Bottom Right (Pattern-based Anomaly Detection):
  • “Anomaly Detection with Predict-Pattern”
  • Compares predicted patterns with actual data to detect anomalies
  • The area marked with a green circle shows where actual data deviates from the predicted pattern

The system detects anomalies in two ways:

  1. When values exceed predetermined thresholds
  2. When actual data significantly deviates from predicted patterns

This type of system is particularly useful in:

  • Industrial monitoring
  • Equipment maintenance
  • Early warning systems
  • Quality control
  • System health monitoring

The combination of prediction and dual detection methods (threshold and pattern-based) provides a robust approach to identifying potential issues before they become critical problems.

AI Prediction

Posted on by lechuck park

From Claude with some prompting
This diagram illustrates an AI Prediction System workflow, which is divided into two main sections:

  1. Upper Section (VIEW):
  • Starts with a UI/UX interface
  • Executes queries with tags (metadata)
  • Connects to time series data storage
  • Displays data visualization charts
  • Includes model selection step
  • Finally generates prediction charts
  1. Lower Section (Automation):
  • Selected ID
  • Selected Model
  • Periodic, new tags and additional configuration
  • Batch work processing (consisting of 4 steps):
    1. Registering
    2. Read Data
    3. Generate Predictions
    4. Add Tag
  • Writing new time series data

The system provides two main functionalities:

  1. A user interface for direct data viewing and prediction execution
  2. Automated batch processing for periodic predictions and data updates

Key Components:

  • Time Series Data storage as a central database
  • View Chart for data visualization
  • Model Selection with time selection (learn & predict)
  • Predict Chart as the final output
  • Batch Works system for automated processing

The workflow demonstrates a comprehensive approach to handling both manual and automated AI predictions, combining user interaction with systematic data processing and analysis. The system appears designed to handle time series data efficiently while providing both immediate and scheduled prediction capabilities.

Easy Prediction

Posted on by lechuck park

From Claude with some prompting
This image illustrates three main approaches to prediction and pattern recognition.

First, for easy prediction, a linear regression model (Linear Regression, y=ax+b) can be used. This is represented by a simple upward trendline. While a basic concept, it is emphasized that this can cover 90% of cases.

Second, for learning complex patterns that recur over time, an AI model is required. This is depicted by the jagged line shape.

Third, for real-time anomaly detection, sudden spike patterns need to be identified.

Additionally, at the bottom of the image, a new phrase has been added: “More & More & More learning makes More & More & More better AI model.” This conveys the idea that as an AI model learns from more and more data, its performance continues to improve.

In summary, the image highlights a step-by-step approach: starting with simple concepts to build a foundation, then utilizing AI models to learn complex patterns, and continuously improving the models through ongoing data learning and training. The key emphasis is on starting with the basics, while recognizing the potential of advanced AI techniques when combined with extensive learning from data.

Trend & Prediction

Posted on by lechuck park

From Claude with some prompting
The image presents a “Trend & Predictions” process, illustrating a data-driven prediction system. The key aspect is the transition from manual validation to automation.

  1. Data Collection & Storage: Digital data is gathered from various sources and stored in a database.
  2. Manual Selection & Validation: a. User manually selects which metric (data) to use b. User manually chooses which AI model to apply c. Analysis & Confirmation using selected data and model
  3. Transition to Automation:
    • Once optimal metrics and models are confirmed in the manual validation phase, the system learns and switches to automation mode. a. Automatically collects and processes data based on selected metrics b. Automatically applies validated models c. Applies pre-set thresholds to prediction results d. Automatically detects and alerts on significant predictive patterns or anomalies based on thresholds

The core of this process is combining user expertise with system efficiency. Initially, users directly select metrics and models, validating results to “educate” the system. This phase determines which data is meaningful and which models are accurate.

Once this “learning” stage is complete, the system transitions to automation mode. It now automatically collects, processes data, and generates predictions using user-validated metrics and models. Furthermore, it applies preset thresholds to automatically detect significant trend changes or anomalies.

This enables the system to continuously monitor trends, providing alerts to users whenever important changes are detected. This allows users to respond quickly, enhancing both the accuracy of predictions and the efficiency of the system.