Predictive analytics is a branch of advanced analytics used to make predictions about unknown future events. Predictive analytics uses techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions. Those predictions are based on patterns found in historical and transactional data in order to identify possible opportunities, trends, or risks in the future. Through predictive analytics, organizations work to increase their revenue, increase or create a competitive advantage, and develop insights to improve economic performance and develop organizational differentiation.
In an example, such as energy load forecasting used to predict energy demand, the workflow to develop a predictive analytics model uses vast amounts of data from energy producers, grid operators, historical load data, and energy traders. The data is then cleaned to remove outliers, data spikes, missing data, or anomalous points to create a unified energy load, temperature, and dew point data. Then the data is used to develop an accurate predictive model using statistics, curve fitting tools, or machine learning in order to build and train a predictive model and a related neural network. New data can then be used to test how well a predictive model performs. Finally, the model can be integrated into a load forecasting system in a production environment to understand the accuracy of the model, and once tuned, can be used in production and forecasting systems.
Predictive analytics are not a monolith; there are different models developed for design-specific functions. These include forecast models, classification models, outlier models, time series models, and clustering models.
Predictive analytic model types
These models are a common predictive analytics model that work by categorizing information based on historical data. These models are used in different industries because they can easily be retrained with new data and provide a broad analysis for answering questions. Classification models are often used in industries like finance and retail.
These models take data and sort it into different groups based on common attributes. The ability to divide data into different datasets based on specific attributes is useful in specific applications, like marketing, where marketers can divide a potential customer base based on common attributes.
These models are one of the most common predictive analytics models, which handle metric value predictions by estimating the values of new data based on learnings from historical data. They are often used to generate numerical values in historical data when there is none to be found. One of the strengths of forecast models is the ability to input multiple parameters.
These models, unlike classification and forecast models that work with historical data, work with anomalous data within a data set and by identifying the unusual data in isolation or in relation with different categories. These models are often useful in industries where identifying models are effective in detecting fraud based on their abilities to find anomalies. And since an incidence of fraud is a deviation from the norm, the outlier model can likely predict fraud before it occurs and, in the case of financial or transactional fraud, can assess the amount of money lost, location, purchase history, time, and the nature of the purchase.
Time series model
This is a model that focuses on data in which time is the input parameter. The time series model works by using different data points to develop a numerical metric that works to predict trends within a specified period. This model can be used to see how particular variables as they can change over time.
Predictive models are used for all kinds of applications, including weather forecasting, creating challenging and engaging video games, and translating voice to text for mobile phone messaging. These applications use statistical models of existing data to make predictions about future data. Descriptive models can determine relationships, patterns, and structures in data that can draw conclusions about how changes in the underlying processes that generate the data will change the results. Predictive models build on these descriptive models to determine the likelihood of certain future outcomes given current conditions or a set of expected future conditions. Predictive models have been used in industries such as finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing.
Predictive analytics use cases
Especially when used in manufacturing and utilization of resources in the supply chain, predictive modeling can be used to clean and optimize the quality of data used for forecasting in inventory management and supply chain optimization.
Combining analytics methods can improve pattern detection and prevent criminal behavior. Behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities, and persistent threats.
Predictive analytics is used to determine customer responses or purchases, as well as promote cross-sell opportunities, and help businesses attract, retain, and grow profitable customers.
Companies can use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue, and predictive analytics can enable organizations to function more efficiently.
Predictive analytics can be used for organizations to predict when routine equipment maintenance will be required and can be scheduled before a problem or malfunction arises.
Predictive analytics can be used in credit scores, insurance claims, and debt collection to assess and determine the likelihood of future defaults and manage risk when lending.
Active traders use a variety of metrics based on past events in the decision making process on whether to buy or sell a security. Moving averages, bands, and breakpoints are all based on historical data and are used to forecast future price movements.
Predictive analytics and its application has in some cases been criticized and legally restricted. This has largely been in cases where the use of predictive analytics has resulted in perceived inequities of its outcomes. This has most commonly been in situations when predictive models have resulted in statistical discrimination against racial or ethnic groups in areas such as credit scoring, home lending, employment, or risk of criminal behavior. A famous, and illegal, example of this is the practice of redlining in home lending by banks. Because of incidents and practices such as redlining, any predictive analytics models that include information such as a person's race are now often excluded from predictive analytics.
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