![\"P2_RGB](\"https:\/\/oihelp.corporate.ifs.com\/help\/wp-content\/uploads\/2015\/02\/P2_RGB-Explorer-Icon.png\"){kind=icon}
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P2 Explorer version 4.7 is a minor release that includes model-based analysis, which extends the capabilities of the Trend to leverage and fit models directly against trend data.<\/p>\n
Our new Edge Adaptor provides the infrastructure to allow cloud-hosted implementations of P2 Explorer to get data from on-premise datasources, and also provides an avenue for addressing latency issues in some implementations.<\/p>\n
Explorer 4.7 also provides the foundation for full integration of the new breed of Explorer modules, forming the cornerstone of our \u2018single event\u2019 philosophy.<\/p>\n
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The new Analysis panel in the Explorer Trend extends the capabilities of the trend to enable the creation and visualization of complex model directly against any timeseries data in the system.<\/p>\n
The first models that have been provided are line fitting methods (linear, polynomial, and logarithmic). These will be expanded to include many other types of models.<\/p>\n
When an analysis has been created and is saved, the output of the model is saved as a data tag.<\/p>\n These tags can be used anywhere in the system where datasource tags are used, such as in calculations, on dashboards, or in Sentinel monitors. As an analysis is saved as a tag, you can also add comments to them.<\/p>\n The Analysis button in the ribbon allows you to toggle the Analysis panel on and off. You can also add a new analysis directly from the Trace Table by clicking the + button.<\/p>\n When adding a new analysis, first choose the model type. There are 3 default model types available: Linear, Logarithmic, and Polynomial.<\/p>\n The edit panel offers a 3-step process to training and executing an analysis:<\/p>\n Effective Range allows you to specify the period of time for which this analysis is valid.<\/p>\n You can specify the exact dates manually, or you can use a hairline to provide the dates.<\/p>\n The Training section is where you provide the data and the date range against which you want to train the model. As with Effective Range, you can specify the exact dates manually, or you can use a hairline to provide the dates.<\/p>\n There is also an option to filter the data \u2013 you can apply a standard deviation filter or an interquartile filter. This provides an easy way to exclude data outliers and ensure that only clean data is used in training.<\/p>\n The model parameters provide an opportunity to modify the polynomial coefficients after training, before executing the model. This provides a way to manipulate the data to determine the best result before executing the analysis.<\/p>\n When configuring a bar chart, there is now an option to show the value of each bar. The bar value is displayed in white text at the top of the bar. Note that this is only available on bar charts.<\/p>\n In Explorer Studio, when configuring the chart, this option is available as the \u2018Show Value<\/strong>\u2019 option for the series.<\/p>\n Note that if the value is too low, there will be no space for the number and the value will therefore be hidden.<\/p>\n![\"\"](\"https:\/\/oihelp.corporate.ifs.com\/help\/wp-content\/uploads\/2019\/11\/image001.png\")
<\/a><\/p>\nEffective Range<\/h3>\n
<\/a><\/p>\nTraining<\/h3>\n
<\/a><\/p>\nModel Parameters<\/h3>\n
<\/a><\/p>\nLabels in Bar Graphs<\/h2>\n
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