Composite Model Analysis of Time Series Data Having Irregular Trends for Anomaly Detection

A method for detecting anomalies in time series data exhibiting irregular trends, comprising the steps of:

• collecting data at a data inception point using a data collection module, ensuring comprehensive data capture from various sources including IoT devices, transaction logs, and sensor networks;
• logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis by capturing every data point with precise timestamps;
• extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module, which involves removing redundant or irrelevant data entries and focusing on critical metrics;
• performing preliminary analysis on the extracted data with an analysis module to identify any obvious patterns or anomalies and establish a baseline understanding, using statistical methods and visual inspections;
• cleaning the extracted data using a data cleanup module to remove noise and irrelevant information, ensuring that the data used for feature engineering and analysis is accurate and reliable, involving methods such as outlier removal, interpolation to fill missing values, and normalization to standardize data scales;
• engineering features from the cleaned data by transforming raw data into a structured format that highlights significant attributes and properties for machine learning algorithms using a feature engineering module, including creating new variables, aggregating data points, and encoding categorical variables into numerical format;
• ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module, ensuring that the data is formatted and stored in a database or data warehouse for easy access;
• retrieving the stored data for detailed trend analysis using sophisticated algorithms to detect regularities and irregularities with a data retrieval module, employing SQL queries or API calls to access specific datasets;
• identifying unique trends and patterns within the data by analyzing temporal sequences and external influencers such as holidays or weekends using a trend analysis module, applying time-series analysis techniques like moving averages, seasonal decomposition, and Fourier transforms;
• clustering the data based on identified trend influencers to categorize the data into distinct segments representing different patterns using a clustering module, which can include methods such as K-means clustering, hierarchical clustering, or DBSCAN to group similar data points;
• developing a first-order predictive model to predict the general trend or specific value within the data using a model development module, training machine learning models such as linear regression or AutoRegressive Integrated Moving Average (ARIMA);
• developing a second-order predictive model to predict the differences identified by the first-order model using a second-order model development module, employing more complex models such as Random Forest or Gradient Boosting to capture residual patterns specific to each cluster based on the identified trend influencers including holidays or weekends;
• continuing hierarchical modeling with higher-order models to successively predict residuals left by previous models using higher-order model development modules, ensuring that even small irregular trends are captured accurately by iterative refinements for each cluster based on the identified trend influencers including holidays or weekends;
• serializing the predictive models for efficient storage and retrieval, ensuring that models can be easily accessed and applied to new data using a model serialization module, utilizing formats like PMML (Predictive Model Markup Language) or ONNX (Open Neural Network Exchange) to facilitate interoperability;
• classifying new data points to determine a relevant trend cluster by comparing them to historical data patterns using a classification module, employing supervised learning algorithms such as decision trees or support vector machines to assign new data points to a correct cluster;
• selecting and loading an appropriate predictive model for the classified data point based on the identified trend cluster using a model selection and loading module, ensuring a correct model is applied for anomaly detection by dynamically linking data points with their respective models;
• comparing the classified data point against an expected pattern predicted by the model to detect deviations using a comparison module, involving calculating residuals and applying threshold criteria to identify anomalies;
• determining whether the data point is an anomaly based on the comparison, identifying significant deviations from expected patterns using an anomaly detection module, which may involve statistical tests, machine learning classifiers, or ensemble methods to ensure robust detection;
• generating an automated response if an anomaly is detected, which may include sending alerts or initiating remedial actions using an automated response module, providing notifications via email, short message service (SMS), or system logs, and triggering automated workflows or corrective actions to mitigate potential issues;
• concluding without further action if no anomaly is detected, allowing the system to continue monitoring and analyzing incoming data using a monitoring module, ensuring continuous surveillance and anomaly detection capabilities;
• and
• continuously updating and refining the first-order, second-order, and higher-order predictive models based on latest data patterns and trends using the model development module to ensure adaptability and accuracy over time, leveraging continuous learning algorithms to keep the models current and effective for each cluster.

A method for detecting anomalies in time series data exhibiting irregular trends, comprising:

• collecting data at a data inception point using a data collection module, ensuring comprehensive data capture from various sources, including Internet of Things (IoT) devices, transaction logs, and sensor networks, to provide a diverse dataset for robust analysis;
• logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis, capturing every data point with precise timestamps to create a reliable time series;
• extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module, removing redundant or irrelevant data entries and focusing on critical metrics including transaction amounts, device activity levels, and sensor readings;
• performing preliminary analysis on the extracted data with an analysis module to identify obvious patterns or anomalies and establish a baseline understanding, using statistical methods including mean, median, and standard deviation, and visual inspections through graphs and charts;
• cleaning the extracted data using a data cleanup module to remove noise and irrelevant information, ensuring the data used for feature engineering and analysis is accurate and reliable, by applying methods including outlier removal, interpolation to fill missing values, and normalization to standardize data scales;
• engineering features from the cleaned data by transforming raw data into a structured format that highlights significant attributes and properties for machine learning algorithms using a feature engineering module, including creating new variables, aggregating data points, and encoding categorical variables into numerical format;
• ingesting the engineered features into a storage and primary data source for organized and efficient retrieval using a data ingestion module, formatting and storing the data in a database or data warehouse for easy access and retrieval;
• retrieving the stored data for detailed trend analysis using sophisticated algorithms to detect regularities and irregularities with a data retrieval module, employing SQL queries or API calls to access specific datasets efficiently;
• identifying unique trends and patterns within the retrieved data by analyzing temporal sequences and external influencers including holidays or weekends using a trend analysis module, applying time-series analysis techniques including moving averages, seasonal decomposition, and Fourier transforms to uncover hidden patterns;
• clustering the retrieved data based on the identified trend influencers to categorize the data into distinct segments representing different patterns using a clustering module, applying methods including K-means clustering, hierarchical clustering, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to group similar data points;
• developing a first-order predictive model for each distinct segment to predict a general trend or specific value within the data using a model development module, training machine learning models including linear regression or AutoRegressive Integrated Moving Average (ARIMA) to capture an overall trend specific to each distinct segment based on the identified trend influencers including holidays or weekends;
• developing a second-order predictive model for each distinct segment to predict differences identified by the first-order predictive model using a second-order model development module, employing machine learning models including Random Forest or Gradient Boosting to capture residual patterns specific to each distinct segment based on the identified trend influencers including holidays or weekends;
• continuing hierarchical modeling with higher-order predictive models for each distinct segment to successively predict residuals left by previous models using higher-order model development modules, ensuring small irregular trends are captured accurately by iterative refinements specific to each distinct segment based on the identified trend influencers including holidays or weekends;
• serializing the first-order, second-order, and higher-order predictive models for efficient storage and retrieval using a model serialization module, utilizing formats including Predictive Model Markup Language (PMML) or Open Neural Network Exchange (ONNX) to facilitate interoperability;
• classifying new data points to determine a relevant distinct segment by comparing the new data points to historical data patterns using a classification module, employing supervised learning algorithms including decision trees or support vector machines to assign the new data points to a correct distinct segment;
• selecting and loading an appropriate predictive model for each classified data point based on the relevant distinct segment using a model selection and loading module, ensuring a correct model specific to the identified trend influencers is applied for anomaly detection by dynamically linking the classified data point with the appropriate predictive model;
• comparing each classified data point against an expected pattern predicted by the selected predictive model to detect deviations using a comparison module, calculating residuals and applying threshold criteria to identify anomalies;
• determining whether each classified data point is an anomaly based on the comparison, identifying significant deviations from expected patterns using an anomaly detection module, applying statistical tests, machine learning classifiers, or ensemble methods to ensure robust detection;
• generating an automated response if an anomaly is detected, including sending alerts or initiating remedial actions using an automated response module, providing notifications via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions to mitigate potential issues;
• concluding without further action if no anomaly is detected, allowing continuous monitoring and analyzing of incoming data using a monitoring module, ensuring continuous surveillance and anomaly detection capabilities, and
• continuously updating and refining the first-order, second-order, and higher-order predictive models based on latest data patterns and trends using the model development module to ensure adaptability and accuracy over time, leveraging continuous learning algorithms to keep the models current and effective for each distinct segment.

A method for detecting anomalies in time series data exhibiting irregular trends, comprising:

• collecting data at a data inception point using a data collection module, ensuring comprehensive data capture from various sources including Internet of Things (IoT) devices, transaction logs, sensor networks, social media feeds, weather sensors, and financial market data, to provide a diverse and holistic dataset for robust analysis, wherein the data collection module gathers data reflecting a wide range of operational conditions and external influences;
• logging the collected data in real-time using a real-time logging tool to maintain temporal integrity and allow for accurate sequence analysis, capturing every data point with precise timestamps to create a reliable time series, wherein the real-time logging tool creates a detailed chronological record of events essential for understanding the context of detected anomalies;
• filtering the collected data to isolate pertinent information before logging using a data filtering module, applying advanced filtering techniques including range checks, duplication removal, and significance tests to enhance data quality;
• extracting relevant data from the logged data by filtering and isolating key data points necessary for further analysis using an extraction module, removing redundant or irrelevant data entries and focusing on critical metrics including transaction amounts, device activity levels, and sensor readings, wherein the extraction module streamlines the dataset by focusing on pertinent information for subsequent stages;
• performing preliminary analysis on the extracted data with an analysis module to identify obvious patterns or anomalies and establish a baseline understanding, using statistical methods including mean, median, and standard deviation, and visual inspections through techniques including histogram analysis, scatter plots, and time-series plotting, wherein the preliminary analysis detects initial irregularities to provide an early indication of potential anomalies;
• cleaning the extracted data using a data cleanup module to remove noise and irrelevant information, ensuring the data used for feature engineering and analysis is accurate and reliable, by applying methods including outlier removal through Z-score filtering, interpolation through linear interpolation, and normalization through min-max scaling to standardize data scales, wherein the data cleanup module eliminates extreme values that could skew the analysis, ensures continuity in the data, and adjusts scales of different data attributes to a common range;
• engineering features from the cleaned data by transforming raw data into a structured format that highlights significant attributes and properties for machine learning algorithms using a feature engineering module, including creating new variables that capture relevant information, aggregating data points over specific time intervals, encoding categorical variables into numerical format such as one-hot encoding for day of the week, and applying domain-specific transformations including time-based aggregations, one-hot encoding, and polynomial feature creation, wherein the feature engineering module exposes underlying trends and relationships not immediately apparent in the raw data;
• ingesting the engineered features into a primary storage source using a data ingestion module, formatting and storing the data in a database or data warehouse compatible with big data frameworks including Hadoop or Spark for organized and efficient retrieval, wherein the data ingestion module organizes the data for easy access in the trend analysis phase;
• retrieving the stored data for detailed trend analysis using sophisticated algorithms to detect regularities and irregularities with a data retrieval module, employing SQL queries or API calls to access specific datasets efficiently;
• identifying unique trends and patterns within the retrieved data by analyzing temporal sequences and external influencers including holidays or weekends using a trend analysis module, applying time-series analysis techniques including moving averages, seasonal decomposition through Seasonal and Trend decomposition using Loess (STL), Fourier transforms, exponential smoothing, and frequency domain analysis to uncover hidden patterns, wherein the trend analysis module separates the data into trend, seasonal, and residual components to facilitate anomaly identification;
• clustering the retrieved data into micro-clusters based on intersections of different trends influenced by the external influencers including holidays or weekends using a clustering module, applying clustering methods including K-means clustering by minimizing variance within each cluster, hierarchical clustering to create a tree of clusters based on data similarity, Gaussian Mixture Models, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify clusters based on density, and optimizing cluster parameters using silhouette analysis, dendrograms, and density-based spatial clustering to group similar data points, wherein the clustering module manages and analyzes data with varying characteristics more effectively;
• developing a first-order predictive model for each micro-cluster to predict a general trend or specific value within the data using a model development module, training machine learning models including linear regression to model relationships between variables, AutoRegressive Integrated Moving Average (ARIMA) to account for autocorrelation in time series data, or neural networks to capture an overall trend specific to each micro-cluster based on the external influencers;
• developing a second-order predictive model for each micro-cluster to predict differences identified by the first-order predictive model using a second-order model development module, employing machine learning models including Random Forest to create multiple decision trees and merge results for improved accuracy and avoidance of overfitting, or Gradient Boosting to build models sequentially with each correcting errors of its predecessor, to capture residual patterns specific to each micro-cluster based on the external influencers;
• continuing hierarchical modeling with higher-order predictive models for each micro-cluster to successively predict residuals left by previous models using higher-order model development modules, ensuring small irregular trends are captured accurately by iterative refinements specific to each micro-cluster based on the external influencers including holidays or weekends, wherein the higher-order model development modules apply ensemble methods including bagging, boosting, and stacking to enhance accuracy and robustness by combining multiple models;
• serializing the first-order, second-order, and higher-order predictive models for efficient storage and retrieval using a model serialization module, utilizing formats including Predictive Model Markup Language (PMML) as an XML-based standard for representing predictive models to enable sharing between applications, or Open Neural Network Exchange (ONNX) for interoperability between various frameworks, to facilitate interoperability and cross-platform deployment;
• classifying new data points to determine a relevant micro-cluster by comparing the new data points to historical data patterns using a classification module, employing supervised learning algorithms including decision trees, support vector machines, or nearest neighbors to assign the new data points to a correct micro-cluster based on characteristics such as weekend sales spikes;
• selecting and loading an appropriate predictive model for each classified data point based on the relevant micro-cluster using a model selection and loading module, ensuring a correct model specific to the external influencers is applied for anomaly detection by dynamically linking the classified data point with the appropriate predictive model;
• comparing each classified data point against an expected pattern predicted by the selected predictive model to detect deviations using a comparison module, calculating residuals as differences between observed and predicted values and applying threshold criteria to identify anomalies, wherein the threshold criteria determine significant deviations from the expected pattern;
• determining whether each classified data point is an anomaly based on the comparison, identifying significant deviations from expected patterns using an anomaly detection module, applying statistical tests including Z-scores or hypothesis testing, machine learning classifiers including support vector machines or neural networks, or ensemble methods to ensure robust detection;
• generating an automated response if an anomaly is detected, including sending alerts or initiating remedial actions using an automated response module, providing notifications via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions through integration with incident management systems and automated resolution protocols to mitigate potential issues, based on the nature and severity of the anomaly;
• continuously updating and refining the first-order, second-order, and higher-order predictive models based on latest data patterns and trends using the model development module to ensure adaptability and accuracy over time, wherein the model development module implements continuous model retraining pipelines;
• leveraging continuous learning algorithms to keep the first-order, second-order, and higher-order predictive models current and effective for each micro-cluster, including delivering artificial intelligence enhancements over time through iterative processes;
• and
• concluding without further action if no anomaly is detected, allowing continuous monitoring and analyzing of incoming data using a monitoring module, ensuring continuous surveillance and anomaly detection capabilities, wherein the monitoring module maintains ongoing effectiveness by promptly identifying and addressing new anomalies.

A computer-implemented method for detecting anomalies in time series data exhibiting irregular trends performed by a computing device that includes a processor and a non-volatile memory storing computer-executable instructions, the method comprising:

• collecting, by a data collection module, data at a data inception point, ensuring comprehensive capture of the data from various sources including Internet of Things (IoT) devices, transaction logs, sensor networks, social media feeds, weather sensors, and financial market data, to provide a diverse and holistic dataset for robust analysis;
• logging, by a real-time logging tool, the collected data in real-time to maintain temporal integrity of the collected data and allow for accurate sequence analysis of the collected data, capturing every data point with precise timestamps to create a reliable time series;
• filtering, by a data filtering module, the collected data to isolate pertinent information before logging the collected data, applying advanced filtering techniques including range checks, duplication removal, and significance tests to enhance quality of the collected data;
• extracting, by an extraction module, relevant data from the logged data by filtering and isolating key data points necessary for further analysis, removing redundant or irrelevant data entries and focusing on critical metrics including transaction amounts, device activity levels, and sensor readings;
• performing, by an analysis module, preliminary analysis on the extracted data to identify obvious patterns or anomalies in the extracted data and establish a baseline understanding of the extracted data, using statistical methods including mean, median, and standard deviation, and visual inspections through techniques including histogram analysis, scatter plots, and time-series plotting;
• cleaning, by a data cleanup module, the extracted data to remove noise and irrelevant information from the extracted data, ensuring that the data used for feature engineering and analysis is accurate and reliable, by applying methods including outlier removal through Z-score filtering, interpolation through linear interpolation, and normalization through min-max scaling to standardize data scales;
• engineering, by a feature engineering module, features from the cleaned data by transforming raw data into a structured format that highlights significant attributes and properties for machine learning algorithms, including creating new variables, aggregating data points over specific time intervals, encoding categorical variables into numerical format, and applying domain-specific transformations including time-based aggregations, one-hot encoding, and polynomial feature creation;
• ingesting, by a data ingestion module, the engineered features into a primary storage source, formatting and storing the data in a database or data warehouse compatible with big data frameworks including Hadoop or Spark for organized and efficient retrieval;
• retrieving, by a data retrieval module, the stored data for detailed trend analysis using sophisticated algorithms to detect regularities and irregularities in the stored data, employing SQL queries or API calls to access specific datasets efficiently;
• identifying, by a trend analysis module, unique trends and patterns within the retrieved data by analyzing temporal sequences and external influencers including holidays or weekends, the trend analysis module employing a large language model to perform natural language processing and pattern recognition on the retrieved data, applying time-series analysis techniques including moving averages, seasonal decomposition through Seasonal and Trend decomposition using Loess (STL), Fourier transforms, exponential smoothing, and frequency domain analysis to uncover hidden patterns;
• clustering, by a clustering module, the retrieved data into micro-clusters based on intersections of different trends influenced by the external influencers including holidays or weekends, applying clustering methods including K-means clustering, hierarchical clustering, Gaussian Mixture Models, or Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and optimizing cluster parameters using silhouette analysis, dendrograms, and density-based spatial clustering to group similar data points;
• developing, by a model development module, a first-order predictive model for each micro-cluster to predict a general trend or specific value within the retrieved data, training machine learning models including linear regression, AutoRegressive Integrated Moving Average (ARIMA) or neural networks to capture an overall trend specific to each micro-cluster based on the external influencers;
• developing, by a second-order model development module, a second-order predictive model for each micro-cluster to predict differences identified by the first-order predictive model, employing machine learning models including Random Forest or Gradient Boosting to capture residual patterns specific to each micro-cluster based on the external influencers;
• continuing, by higher-order model development modules, hierarchical modeling with higher-order predictive models for each micro-cluster to successively predict residuals left by previous models, ensuring small irregular trends are captured accurately by iterative refinements specific to each micro-cluster based on the external influencers including holidays or weekends;
• serializing, by a model serialization module, the first-order, second-order, and higher-order predictive models for efficient storage and retrieval, utilizing formats including Predictive Model Markup Language (PMML) or Open Neural Network Exchange (ONNX) to facilitate interoperability and cross-platform deployment;
• classifying, by a classification module, new data points to determine a relevant micro-cluster by comparing the new data points to historical data patterns, employing supervised learning algorithms including decision trees, support vector machines, or nearest neighbors to assign the new data points to a correct micro-cluster;
• selecting and loading, by a model selection and loading module, an appropriate predictive model for each classified data point based on the relevant micro-cluster, ensuring a correct model specific to the external influencers is applied for anomaly detection by dynamically linking the classified data point with the appropriate predictive model;
• comparing, by a comparison module, each classified data point against an expected pattern predicted by the selected predictive model to detect deviations, calculating residuals and applying threshold criteria to identify anomalies;
• determining, by an anomaly detection module, whether each classified data point is an anomaly based on the comparison, identifying significant deviations from expected patterns, applying statistical tests, machine learning classifiers, or ensemble methods to ensure robust detection;
• generating, by an automated response module, an automated response if an anomaly is detected, including sending alerts or initiating remedial actions, providing notifications via email, Short Message Service (SMS), or system logs, and triggering automated workflows or corrective actions through integration with incident management systems and automated resolution protocols to mitigate potential issues;
• continuously updating and refining, by the model development module, the first-order, second-order, and higher-order predictive models based on latest data patterns and trends to ensure adaptability and accuracy over time;
• leveraging, by the model development module, continuous learning algorithms to keep the first-order, second-order, and higher-order predictive models current and effective for each micro-cluster;
• and
• concluding, by a monitoring module, without further action if no anomaly is detected, allowing continuous monitoring and analyzing of incoming data, ensuring continuous surveillance and anomaly detection capabilities.