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Time series prediction uses a model to predict future values based on previously observed values. Time series data differs somewhat from non-time series data in both the formatting of the data and the training of predictive models. This article will highlight several important considerations when dealing with time series data. Preparing Time Series Data: The data must contain exactly one field with Op Type “TEMPORAL” and one field with Op Type “ENTITY_ID”, which defines the identifier for an entity, such as a machine serial number. The ENTITY_ID field should remain the same as long as there are no missing timestamps and it is within the same asset but should be different for different assets or asset runs in order to accurately assign history during model training and scoring.     The TEMPORAL field is a numeric field indicating the order of the data rows for a specific entity . One should also ensure that data is formatted such that the timestamps are equally spaced (for example, one data point every minute) and that no gaps exist in the sequence of numbers.   If there are gaps in the time series data, it is recommended to restart the series after the gap as a new entity. Alternatively, if the gap is small enough (few data points), linear interpolation based on the gap endpoint values within the same entity is generally acceptable.   Model Creation in Time Series: When creating a timeseries model in Analytics Builder, you will be asked to specify a lookback size and lookahead parameter. The lookback size determines how many historical datapoints (including the current row) will be used in the model. The lookahead indicates how many time steps ahead to predict.  If the value of the goal variable is not known at time of scoring, unchecking Use Goal History will use the goal column during training but not its history during scoring.   Time Series models can also be created in Services using the Training Thing. The lookback size and lookahead parameter are specified in the CreateJob service. The virtualSensor field is used to indicate if the model should be trained to predict values for a field that will not be available during scoring. For example, one can train a time series model to predict Volume using evolving Temperature and Pressure, based on sensor data for these three variables over a period of time. However, the Volume sensor may be removed from further assets in order to reduce costs, and the predictive model can be used instead.   Two important considerations: ThingWorx Analytics will expand historical data in the time series into new columns. This process creates new features using the values of the previous time steps. Additionally, low order derivatives, together with average and standard deviation features are computed over small contiguous subgroups of the historical data.   The expansion process can make the dataset exceptionally wide, so time series training is generally significantly slower compared to training with no history on the same dataset. This gets exacerbated when lookback size = 0 (auto-windowing, a process where the system is trying to find the optimal lookback). If there are columns that are not changing or change infrequently (such as a device serial number or zip code of the device’s location), these should be marked as Static when importing the data. Any columns labeled Static will not be expanded to create new features. Care also needs to be taken to exclude any features that are known to not be relevant to the prediction. Using a large lookback can eliminate how many examples / entities the model has available to train. For example, if a lookback of 8 is used, then any entities that have less than 8 examples will not be used in training. For the same reason, scoring for time series produces less results than the number of rows provided as input: if 10 rows are provided and lookback is 6, then only 5 predictions will be produced.

How to score new data with ThingWorx Analytics ?   The following is valid starting with ThingWorx Analytics (TWA) 8.3.0   Overview   Once a training model has been created, one of the main objective is to score new data to predict the value for the goal ThingWorx Analytics can score new data in 2 ways: Batch scoring Real time scoring Batch scoring   Batch scoring will be used when a large amount of data needs to be scored. To perform a batch scoring we will usually follow steps similar to the below ones: Upload the historic data Create a new model with this historic data Upload new data – the one to be scored Perform a prediction job to score those new data Retrieve the prediction job result Uploading the new data can be done in different ways. If using a large amount of data, it can be easier to upload the data via a csv file in a similar way as the historic data. This is the way used in ThingWorx Analytics Builder. If the amount of data is more limited this can be sent in the body of the scoring request. The post Analytics: Prediction Methods Mashup  shows a good example of how to do this using the PredictionThing.BatchScore service. We are focusing below on ThingWorx Analytics Builder, that is uploading new data via a csv file. In order to perform the scoring job only on the new data in step 4 above, we need to be able to filter those added data. If the dataset has already suitable column/feature such as a timestamp for example, we can use this to score only new data after timestamp > newdate, assuming all data are in chronological order. If the dataset has no such feature, we will have to add one  beforehand when we first upload the historic data in step 1 above. We often use a new column/feature named record_purpose to this effect. So initial data can take a value of training for this record_purpose feature since they are used to create the initial model. Then new added data to be scored can get any value that identify those rows only. It is important to note that this record_purpose feature needs to be set with the optType INFORMATIONAL so as to not be taken into account by the learning algorithms.   The video below shows those steps while using ThingWorx Analytics Builder   Real time scoring   Real time scoring is better suited for small amount of data. The process for real time scoring can be done either via the Analytics Server PredictionThing RealTimeScore service or using the Analytics Manager framework. The posts How to work with ordinal and categorical data in ThingWorx Analytics  and Analytics: Prediction Methods Mashup do give  examples of the use of the RealTimeScore service.   We will concentrate below on the Analytics Manager. The process involves the following steps: In Analytics Manager Create an Analysis Provider that uses the AnalyticsServerConnector connector Publish the model created in ThingWorx Analytics Builder to Analytics Manager Enable the model created Create an Analysis Event Map the properties to the datashape field Enable the Event In ThingWorx Composer Relevant properties of the Thing used in the Analysis Event are updated in someway This trigger the analysis job to be executed The scoring result is populated into the result property mapped in the Analysis event The Help Center has got more detailed about this process. The following video shows those steps Following articles can also be of interest for this topic: How to use ThingPredictor in release 8.3 of ThingWorx Analytics Server ? Publish model from Analytics Builder into Analytics Manager using TW.AnalysisServices.AnalyticsServer.AnalyticsServerConnector Creating Template For Thing, And Configure Analysis Event For Real-Time Scoring via Analytics Manager Note that the AnalyticsServerConnector connector in release 8.3 replaces the ThingPredictor connector from previous releases.

ThingWorx 8.3 covers the following areas of the product portfolio:  ThingWorx Analytics, ThingWorx Utilities, and ThingWorx Foundation which includes Connection Server and Edge capabilities.   Highlights of the release include:   ThingWorx Foundation Next Generation Composer: Now default admin and developer interface Full Feature parity with legacy Composer New capability for User and Group administration, Authorization and permissions, Export, Monitoring and Logging. More in Helpcenter Localization support for German and French Mashup Builder: JQuery 3 upgrade Grid Advanced Extension now supports Cell Editing and Footers Platform: Active Directory (AD) Integration enhancements for larger AD forests and user extension field mapping Upgrade in-place enhancements for Java SDK developers Developer Enablement Capture the usage statics such as time taken to execute a ThingWorx service, # of times a service runs in ThingWorx using Service Utilization Statistics functionality powered by all new and efficient Utilization Subsystem. Collect ThingWorx system data such as ESAPI configuration, ThingworxStorage logs, licensing, and JVM information to better diagnose system issues Service Utilization Statistics: ThingWorx Support Package tool Administrator Password and Password Length New installations of ThingWorx will be required to supply the initial Administrator password of the installer’s choice. That password must be supplied via a new entry in the platform-settings.json file. After the initial installation, the Administrator password should then be changed to a strong password to be used going forward. Additional information. As a step toward industry best practices, the Administrator password and all new passwords will need to be at least 10 characters.  When upgrading to 8.3, passwords from older versions of the platform will not need to be modified, but any new passwords being created will need to be at least 10 characters long. See the installation instructions for complete details.   ThingWorx Analytics New Descriptive Services  Core statistics (min, max, deviation, etc.), data distribution (binning), confidence intervals, and other useful calculations. Frequency analysis and transformation (via fast Fourier transform) for troubleshooting use cases and predictive analytics applications Improves users’ ability to apply logic and derive the following insights from streaming data without constructing complex models or accessing machine learning: Enables platform developers to easily process platform data in their applications and prepare the data for predictions. Statistical Process Control (SPC) Services Provides industry-standard calculations that allow IoT developers to implement SPC “control chart rules” in their applications.  Useful in manufacturing and in monitoring equipment and processes. Supports a wide assortment of rules, including number of points continuously above / below a range, in and out of range, increasing or decreasing trends, or alternating directions. Analytics Workbench Bundles the two Analytics interfaces (Analytics Builder and Manager) into a new Analytics section in Composer. Predictive Analytics Improvements Reduces overall install and administration complexity. Improves handling of time dseries data when used in predictive scoring. Includes a new learner, Support Vector Machines, enhancing the platform’s utility in building Boolean predictions. Includes a new ensemble method, Majority Vote, that improves generated model accuracy. Provides redundancy filtering which can optionally remove redundant information to improve explanatory analytics (Signals) and predictive model training. Now supports time series lookahead configuration, simplifying this type of prediction. Replaces ThingPredictor predictive scoring in Analytics Manager with native Analytics Server scoring: Improves scalability of concurrent jobs. Axeda Compatibility Package IDM Connector Support o   ACP v1.1.0 introduces the IDM Connector which enables Axeda customers to connect their Axeda IDM agents to the ThingWorx platform.  The IDM Connector provides support for registration requests, property updates, faults, events, file uploads and downloads.  Axeda ThingWorx Entity Exporter Update o   ACP v1.1.0 also includes an updated version of Axeda-ThingWorx Entity Exporter (ATEE) which now supports exporting Axeda IDM assets from the Axeda application into a format that can be imported in the ThingWorx Platform.  eMessage Connector Improvements o   Additionally, ACP v1.1.0 includes support for instruction based Software Content Management packages for the eMessage Connector which allows you to download file(s), execute instruction(s) and optionally restart the agent.  The Axeda Compatibility Extension (ACE) has new entities to support the IDM Connector and SCM for the eMesssage Connector.  o   Finally, updated versions of the Axeda Compatibility Extensions (ACE) and the Connection Services Extension (CSE) are included in ACP v1.1.0 and provide an improved workflow for granting permissions to the eMessage and IDM Connectors. ThingWorx Extension Updates Websocket Tunnel Extension Update The Websocket Tunnel Extension was updated for 8.3 to support the upgrade to jQuery3 Grid Advanced 4.0.0 comes with 2 key features: Editing - we now have cell editing support for all basetypes. The previous version had boolean editing; 4.0.0 now includes support for all basetypes. Footers - A footer section can now be added to the Grid to display rolled-up Grid totals. You can perform client-side calculations like count, min, max and average, and it includes support for custom functions. Note - Grid Advanced 4.0.0 only supports ThingWorx 8.3 and above. Custom Charts 3.0.1 12 Bug Fixes Google Maps 3.0.1 General Bug Fixes ThingWorx Utilities With the 8.3 Release, ThingWorx Utilities functionality are being repackaged into ThingWorx Foundation and ThingWorx Asset Advisor.  ThingWorx Workflow will now be available with Foundation.  The functionality from the Asset and Alert Management Utilities will be delivered in ThingWorx Asset Advisor.  ThingWorx Software Content Management capabilities will continue to be available for customer to manage the delivery of Software to their Connected Products.  The naming of “Utilities” is being phased out of the ThingWorx Platform packaging but the key functionality formerly described as ThingWorx Utilities continues to be delivered with version 8.3.   ThingWorx 8.3 Reference Documents ThingWorx Analytics 8.3 Reference Documents ThingWorx Platform 8.3 Release Notes ThingWorx Platform Help Center ThingWorx Edge SDKs and WebSocket-based Edge MicroServer Help Center ThingWorx Connection Services Help Center ThingWorx Analytics Help Center ThingWorx Industrial Connectivity Help Center ThingWorx Utilities Help Center ThingWorx Utilities Installation Guide     ThingWorx eSupport Portal ThingWorx Developer Portal PTC Marketplace   The following items will be available for download from the PTC Software Download site on June 8, 2018. ThingWorx Platform – Select Release 8.3 ThingWorx Utilities – Select Release 8.3 ThingWorx Analytics – Select Release 8.3 ThingWorx Extensions – Select Individual Extensions for download.  Will be available with the next Marketplace refresh

Thing Subscription This post is intended for novice ThingWorx users who wants to understand what the definition of Thing Subscription is and the overall purpose of using Thing Subscriptions.   Definition of a Thing Subscription? A Thing subscription is a script(JavaScript) that is called each time an event occurs. Events are property states which are of end users interest (e.g. temperature) and therefore indicators to kick off some functionality in a Thing subscription when any action needed. Events can e.g. be triggered by an Alert that detects a change or an anomaly in property values. The Thing subscription is explicitly linked to an event and when the event is fired the data is being passed to the subscriber.    Why Use a Thing Subscription? Imagine your machine is running 24 hours 7 days a week with supervised human interaction. If a pump temperature exceeds accepted value it needs to be regulated by the manufacturing department. But no one in the department knows when the temperature will exceed accepted value or drop suddenly therefore, the machines is always sporadically physically supervised by humans which leads to heavy costs for the manufacture. With a Thing Subscription a notification alert email can be sent directly to the department manager who acts based on the email notification.   Thing Subscription must have A Thing subscription must have defined a rule which gets executed when an event occurs. The definition of the rule may accommodate any appropriate business logic.   Thing Subscription example process In this scenario Thing subscription is using a predictive analytics model to detect Data Change or any anomaly values going through a Thing Property. So, based on historical data including failure information, a predictive analytics model begins to analyze run-time values from individual Things/properties to the analytics server. The predictive analytics model detects a pattern which detects past failures, when the analytics model predicts a failure/event based on the analyzed patterns an action is being fired via a Thing subscription. That action could be for ThingWorx to create a service ticket or send a notification email to the service department.   Example of a simple Thing Subscription set-up without using Analytics model to analyze data but instead a build-in ThingWorx alert Below example of Thing Subscription will send a notification email when temperature exceeds defined values from ThingWorx alert configuration. Prerequisites; it is necessary to have a mail server extension imported into the ThingWorx Composer this enables the service department to receive the email notification when an event have occurred. The extension can be downloaded from the marketplace. 1. Create a Thing with the MailServer[i] as the Base Thing template.     2. Create a new Thing and add Properties together with an alert that is triggered when the value exeeds user defined temerature.   3. Enable the Thing Subcriptions by Select Subscription and click +Add Make sure to mark the checkbox Enabled Selecting your Event name and your Property name In the right side of the screen you can enter your script/function that will notify ThingWorx email service to create the email notification Select Done and Save   4. Enable Email notification by selecting Services Provide an name Select Me/Entities Mark Other entity Find your Thing where the MailServer is the Thing Template   5. Then find the SendMessage snippet/script and fill out the snippet with your personal information.   [i] View this blog for more information on how to install the MailServer

Datasets with ordinal or categorical goal cannot currently be used in ThingWorx Analytics Builder. However this is only a UI limitation, ThingWorx Analytics Server can handle those data. It does simply require to use the services from the AnalyticsServer-Training and AnalyticsServer-Prediction things to perform the operations.   This can be done using a mashup or via Rest API call (see https://www.ptc.com/en/support/article?n=CS271485 ) . The below video expands on the mashup solution. Attached are also the entities used during the video and a sample dataset with ordinal goal.     Update for ThingWorx 9.0  The API has changed in 9.0, use the entities Entities-90-3Jun2020.xml for release 9.0  

Preface   In this blog post, we will discuss how to Start and Stop ThingWorx Analytics, as well as some other useful triaging/troubleshooting commands. This applies to all flavors of the native Linux installation of the Application.   In order to perform these steps, you will have to have sudo or ROOT access on the host machine; as you will have to execute a shell script and be able to view the outputs.   The example screenshots below were taken on a virtual CentOS 7 Server with a GUI as ROOT user.     Checking ThingWorx Analytics Server Application Status   1. Change directory to the installation destination of the ThingWorx Analytics (TWA) Application. In the screenshot below, the application is installed to the /opt/ThingWorxAnalyticsServer directory   2. In the install directory, there are a series of folders and files. You can use the ​ls​ command to see a list of files and folders in the installation directory.     a. You will need to go navigate one more level down into the ./ThingWorxAnalyticsServer/bin​ ​directory by using command ​cd ./bin​     b. As you can see above, we used the ​pwd​ command to verify that we are in the correct directory.   3. In the ./ThingWorxAnalyticsServer/bin directory, there should be three shell files: configure-apirouter.sh, configure-user.sh, and twas.sh     a. To run a status check of the application, use the command ./twas.sh status           i. This will provide a list of outputs, and a few warning messages. This is normal, see screenshot below:      b. You will have a series of services, which will have a green active (running) or red not active (stopped).           i. List of services: twas-results-ms.service - ThingWorx Analytics - Results Microservice twas-data-ms.service - ThingWorx Analytics - Data Microservice twas-analytics-ms.service - ThingWorx Analytics - Analytics Microservice twas-profiling-ms.service - ThingWorx Analytics - Profiling Microservice twas-clustering-ms.service - ThingWorx Analytics - Clustering Microservice twas-prediction-ms.service - ThingWorx Analytics - PredictionMicroservice twas-training-ms.service - ThingWorx Analytics - Training Microservice twas-validation-ms.service - ThingWorx Analytics - Validation Microservice twas-apirouter.service - ThingWorx Analytics - API Router twas-edge-ms.service - ThingWorx Analytics - Edge Microservice   Starting and Stopping ThingWorx Analytics   If you encounter any errors or stopped services in the above, a good solution would be to restart the TWA Server application.   There are two methods to restart the application, one being the restart ​command, the other would be using the stop​ and ​start​ commands.   Method 1 - Restart Command:   1. In the same ./ThingWorxAnalyticsServer/bin​ ​directory, run the following command: ./twas.sh restart     a. The output of a successful restart will look like the following: 2. The restart should only take a few seconds to complete   Method 2 - Stop / Start Commands:   1. In the same ./ThingWorxAnalyticsServer/bin​ ​directory, run the following command: ./twas.sh stop 2. After the application stops, run the following command: ./twas.sh start   Note: You can confirm the status of the TWA Server application by following the steps in the "Checking ThingWorx Analytics Server Application Status" section above.

​​​There are four types of Analytics:                                                                 Prescriptive analytics: What should I do about it? Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions.Prescriptive analytics is related to both Descriptive and Predictive analytics. While Descriptive analytics aims to provide insight into what has happened and Predictive analytics helps model and forecast what might happen, Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters. “Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications.”These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures. Prescriptive analytics can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options. Prescriptive analytics can be used in two ways: Inform decision logic with analytics: Decision logic needs data as an input to make the decision. The veracity and timeliness of data will insure that the decision logic will operate as expected. It doesn’t matter if the decision logic is that of a person or embedded in an application — in both cases, prescriptive analytics provides the input to the process. Prescriptive analytics can be as simple as aggregate analytics about how much a customer spent on products last month or as sophisticated as a predictive model that predicts the next best offer to a customer. The decision logic may even include an optimization model to determine how much, if any, discount to offer to the customer. Evolve decision logic: Decision logic must evolve to improve or maintain its effectiveness. In some cases, decision logic itself may be flawed or degrade over time. Measuring and analyzing the effectiveness or ineffectiveness of enterprises decisions allows developers to refine or redo decision logic to make it even better. It can be as simple as marketing managers reviewing email conversion rates and adjusting the decision logic to target an additional audience. Alternatively, it can be as sophisticated as embedding a machine learning model in the decision logic for an email marketing campaign to automatically adjust what content is sent to target audiences. Different technologies of Prescriptive analytics to create action: Search and knowledge discovery: Information leads to insights, and insights lead to knowledge. That knowledge enables employees to become smarter about the decisions they make for the benefit of the enterprise. But developers can embed search technology in decision logic to find knowledge used to make decisions in large pools of unstructured big data. Simulation: ​Simulation imitates a real-world process or system over time using a computer model. Because digital simulation relies on a model of the real world, the usefulness and accuracy of simulation to improve decisions depends a lot on the fidelity of the model. Simulation has long been used in multiple industries to test new ideas or how modifications will affect an existing process or system. Mathematical optimization: Mathematical optimization is the process of finding the optimal solution to a problem that has numerically expressed constraints. Machine learning: “Learning” means that the algorithms analyze sets of data to look for patterns and/or correlations that result in insights. Those insights can become deeper and more accurate as the algorithms analyze new data sets. The models created and continuously updated by machine learning can be used as input to decision logic or to improve the decision logic automatically. Paragmetic AI: ​Enterprises can use AI to program machines to continuously learn from new information, build knowledge, and then use that knowledge to make decisions and interact with people and/or other machines.                                               Use of Prescriptive Analytics in ThingWorx Analytics: Thing Optimizer: Thing Optimizer functionality provides the prescriptive scoring and optimization capabilities of ThingWorx Analytics. While predictive scoring allows you to make predictions about future outcomes, prescriptive scoring allows you to see how certain changes might affect future outcomes. After you have generated a prediction model (also called training a model), you can modify the prescriptive attributes in your data (those attributes marked as levers) to alter the predictions. The prescriptive scoring process evaluates each lever attribute, and returns an optimal value for that feature, depending on whether you want to minimize or maximize the goal variable. Prescriptive scoring results include both an original score (the score before any lever attributes are changed) and an optimized score (the score after optimal values are applied to the lever attributes). In addition, for each attribute identified in your data as a lever, original and optimal values are included in the prescriptive scoring results. How to Access Thing Optimizer Functionality: ThingWorx Analytics prescriptive scoring can only be accessed via the REST API Service. Using a REST client, you can access the Scoring service which includes a series of API endpoints to submit scoring requests, retrieve results, list jobs, and more. Requires installation of the ThingWorx Analytics Server. How to avoid mistakes - Below are some common mistakes while doing Prescriptive analytics: Starting digital analytics without a clear goal Ignoring core metrics Choosing overkill analytics tools Creating beautiful reports with little business value Failing to detect tracking errors                                                                                                                                 Image source: Wikipedia, Content: go.forrester.com(Partially)

A confusion matrix is a technique for summarizing the performance of a classification algorithm. Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your data set. Calculating a confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making. Classification Accuracy and its Limitations: ​Classification Accuracy = Correct Predictions/Total Predictions The main problem with classification accuracy is that it hides the detail you need to better understand the performance of your classification model. Below are two examples: 1.  When you are data has more than 2 classes. With 3 or more classes you may get a classification accuracy of 80%, but you don’t know if that is because all classes are being predicted equally well or whether one or two classes are being neglected by the model. 2.  When your data does not have an even number of classes. You may achieve accuracy of 90% or more, but this is not a good score if 90 records for every 100 belong to one class and you can achieve this score by always predicting the most common class value. Classification accuracy can hide the detail you need to diagnose the performance of your model. But thankfully we can tease apart this detail by using a confusion matrix. Confusion Matrix Terminology: A confusion matrix is a table that is often use to describe the performance of a classification model on a set of test data for which true values are known. Let’s start with an example for a binary classifier: N=165 Predicted no: Predicted yes: Actual no: 50 10 Actual yes: 5 100 What we can learn from Confusion Matrix? There are two possible predicted classes: "yes" and "no". If we were predicting the presence of a disease, for example, "yes" would mean they have the disease, and "no" would mean they don't have the disease. The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease). Out of those 165 cases, the classifier predicted "yes" 110 times, and "no" 55 times. In reality, 105 patients in the sample have the disease, and 60 patients do not. Let's now define the most basic terms, which are whole numbers (not rates): True positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease. True negatives (TN): We predicted no, and they don't have the disease. False positives (FP): We predicted yes, but they don't actually have the disease. (Also known as a "Type I error.") False negatives (FN): We predicted no, but they actually do have the disease. (Also known as a "Type II error.") N=165 Predicted No: Predicted Yes: Actual No: TN=50 FP=10 60 Actual Yes: FN=5 TP=100 105 55 110 This is a list of rates that are often computed from a confusion matrix for a binary classifier: Accuracy: Overall, how often is the classifier correct? 1. (TP+TN)/total = (100+50)/165 = 0.91 Misclassification Rate: Overall, how often is it wrong? 1. (FP+FN)/total = (10+5)/165 = 0.09 2. Equivalent to 1 minus Accuracy 3. Also known as "Error Rate" True Positive Rate: When it's actually yes, how often does it predict yes? 1. TP/actual yes = 100/105 = 0.95 2. Also known as "Sensitivity" or "Recall" False Positive Rate: When it's actually no, how often does it predict yes? 1. FP/actual no = 10/60 = 0.17 Specificity: When it's actually no, how often does it predict no? 1. TN/actual no = 50/60 = 0.83 2. Equivalent to 1 minus False Positive Rate Precision: When it predicts yes, how often is it correct? 1. TP/predicted yes = 100/110 = 0.91 Prevalence: How often does the yes condition actually occur in our sample? 1. Actual yes/total = 105/165 = 0.64

Neural Net is a learning algorithm that is inspired by how the human brain works. For example, imagine you love chocolate cake so much that, you joyfully exercise a bit more during the week just to enjoy that delicious chocolate cake without feeling guilty. But if the weather is terrible there is no way you go exercise, and then you can’t eat the delicious chocolate cake. Although, if your beautiful girlfriend / boyfriend exercise with you, then you ignore the weather and joyfully exercise and then you can enjoy that delicious chocolate cake without feeling guilty. The brain’s nervous system passes information using a synapse structure which allows neurons to pass information to other neurons and finally make a decision. This structure of passing information and decision making is the construction behind neural net algorithm. The data structure provides weights on the edges for the nodes/synapses in a directed graph. For example, our chocolate cake decision making could be translated into: w1=6 Whether or not your girlfriend or boyfriend exercise together with you w2=3 Enjoying delicious chocolate cake w3=2 Weather The high weights for example indicates the condition to have a high influence on your output decision making, while lower weight is not that influential. The illustration below is an example of neural net with 5 different input of information: The edges/arrows represent weights each input/node have a weight associated with it. These weights are applied when training neural net. Three of the inputs could represent 1= Delicious chocolate cake, 2= The weather, 3= Your girlfriend or boyfriend, and the last two inputs could be other information. The output is the condition of the decision determined by the hidden layer. ThingWorx Analytics Server applies neural net with full interconnection layer, which means each value from the input layer is duplicated and sent to each node in the hidden layer, just like in following illustration.

Key Functional Highlights ThingWorx 8.0 covers the following areas of the product portfolio:  ThingWorx Analytics, ThingWorx Utilities and ThingWorx Foundation which includes Core, Connection Server and Edge capabilities. Highlights of the release include: ThingWorx Foundation Native Industrial Connectivity: Enhancements to ThingWorx allow users to seamlessly map data from ThingWorx Industrial Connectivity to the ThingModel. With over 150 protocols supporting thousands of devices, ThingWorx Industrial Connectivity allows users to connect, monitor, and manage diverse automation devices from ThingWorx. With this new capability, users can quickly integrate industrial operations data in IoT solutions for smart, connected operations. Native AWS IoT and Azure IoT Cloud Support: ThingWorx 8 now has deeper, native integration with AWS IoT and Azure IoT Hub clouds so you can gain cost efficiencies and standardize on the device cloud provider of your choice.  This support strengthens the connection between leading cloud providers and ThingWorx. Next Generation Composer: Re-imagined Composer using modern browser concepts to improve developer efficiency including enhanced functionality, updated user interface and optimized workflows. Product Installers:  New, Docker-based product installers for Foundation and Analytics make it easy and fast for customers to get the core platform and analytics server running. Single Sign On (SSO): Provides the ability to login once and access all PTC apps and enterprise systems. License Management: Simple, automated, licensing system for collection, storage, reporting, management and auditing of licensing entitlements. Integration Connectors: Integration Connectors allow Thingworx developers and administrators quick and easy access to the data stored on external ERP, PLM, Manufacturing and other systems to quickly develop applications providing improved Contextualization and Analysis. Thingworx 8.0 delivers ‘OData’ and ‘SAP OData’ connectors plus the ability to connect to generic web services to supplement the ‘Swagger’ and ‘Windchill Swagger’ Connectors released in Thingworx 7.4. An improved mapping tool allows Business Administrators to quickly and easily transform retrieved data into a standard Thingworx format for easy consumption. Includes single sign on support for improved user experience. ThingWorx Analytics Native Anomaly Detection: ThingWorx 8 features more tightly integrated analytics capabilities, including the ability to configure anomaly alerts on properties directly from the ThingWorx Composer. ThingWatcher technology is utilized to increase machine monitoring capabilities by automatically learning normal behavior, continuously monitoring data streams and raising alerts when abnormal conditions are identified. ThingWorx Utilities Software Content Management (SCM) – Auto Retry: Provides the ability to automatically retry delivery of patches to devices if interrupted.  This ensures the ability to successfully update devices.  ThingWorx Trial Edition ThingWorx Trial Edition will be available to internal PTC resources at launch and will be made available externally on the Developer Portal shortly after launch. Developer Enablement: Enhancements have been made to the Trial Edition installation tool, providing a native installation process of the ThingWorx platform including: ThingWorx Foundation ThingWorx Utilities ThingWorx Analytics ThingWorx Industrial Connectivity Documentation ThingWorx 8.0 Reference Documents ThingWorx Analytics 8.0 Reference Documents ThingWorx Core 8.0 Release Notes ThingWorx Core Help Center ThingWorx Edge SDKs and WebSocket-based Edge MicroServer Help Center ThingWorx Connection Services Help Center ThingWorx Industrial Connectivity Help Center ThingWorx Utilities Help Center ThingWorx Utilities Installation Guide ThingWorx Analytics Help Center ThingWorx Trial Edition User Guide Additional information ThingWorx eSupport Portal ThingWorx Developer Portal ThingWorx Marketplace Download The following items are available for download from the PTC Software Download site. ThingWorx Platform – Select Release 8.0 ThingWorx Utilities – Select Release 8.0 ThingWorx Analytics – Select Release 8.0 You can also read this post in the Developer Community from Jeremy Little about the technical changes in ThingWorx 8.0.

Sampling Strategy​ This Blog Post will cover the 4 sampling Strategies that are available in ThingWorx Analytics.  It will tell you how the sampling strategy runs behind the scenes, when you may want to use that strategy, and will give you the pros and cons of each strategy. SAMPLE_WITH_REPLACEMENT This strategy is not often used by professionals but still may be useful in certain circumstances.  When you sample with replacement, the value that you randomly selected is then returned to the sample pool.  So there is a chance that you can have the same record multiple times in your sample. Example Let’s say you have a hat that contain 3 cards with different people’s names on them. John Sarah Tom Let’s say you make 2 random selections. The first selection you pull out the name Tom. When you sample with replacement, you would put the name Tom back into the hat and then randomly select a card again.  For your second selection, it is possible to get another name like Sarah, or the same one you selected, Tom. Pros May find improved models in smaller datasets with low row counts Cons The Accuracy of the model may be artificially inflated due to duplicates in the sample SAMPLE_WITHOUT_REPLACEMENT This is the default setting in ThingWorx Analytics and the most commonly used sampling strategy by professionals.  The way this strategy works is after the value is randomly selected from the sample pool, it is not returned.  This ensures that all the values that are selected for the sample, are unique. Example Let’s say you have a hat that contain 3 cards with different people’s names on them. John Sarah Tom Let’s say you make 2 random selections. The first selection you pull out the name Tom. When you sample without replacement, you would randomly select a card from the hat again without adding the card Tom.  For your second selection, you could only get the Sarah or John card. Pros This is the sampling strategy that is most commonly used It will deliver the best results in most cases Cons May not be the best choice if the desired goal is underrepresented in the dataset UPSAMPLE_AND_SAMPLE_WITHOUT_REPLACEMENT This is useful when the desired goal is underrepresented in the dataset.  The features that represent the desired outcome of the goal are copied multiple times so they represent a larger share of the total dataset. Example Let’s say you are trying to discover if a patient is at risk for developing a rare condition, like chronic kidney failure, that affects around .5% of the US population.  In this case, the most accurate model that would be generated would say that no one will get this condition, and according to the numbers, it would be right 99.5% of the time.  But in reality, this is not helpful at all to the use case since you want to know if the patient is at risk of developing the condition. To avoid this from happening, copies are made of the records where the patient did develop the condition so it represents a larger share of the dataset.  Doing this will give ThingWorx Analytics more examples to help it generate a more accurate model. Pros Patterns from the original dataset remain intact Cons Longer training time DOWNSAMPLE_AND_SAMPLE_WITHOUT_REPLACEMENT This is also useful when the desired goal is underrepresented in the dataset. In downsample and sample without replacement, some features that do not represent the desired goal outcome are removed. This is done to increase the desired features percentage of the dataset. Example Let’s continue using the medical example from above.  Instead of creating copies of the desired records, undesired record are removed from the dataset.  This causes the records where patients did develop the condition to occupy a larger percentage of the dataset. Pros Shorter training time Cons Patterns from the original dataset may be lost

In this Blog, we will share some light about Gradient boost, which is a default algorithm in our Analytics platform. We will touch on: 1) The main purpose of Gradient boost and how the technique works. 2) We will look at advantages and constraint. 3) Last some “nice to know” tips when working with Gradient. Gradient boost is a machine learning technique which main purpose is to help weak prediction models become stronger. Gradient boost works by building one tree at a time, and correct errors made by previously tree. The theory support reweights of edges which allows badly weight edges to get reweighted. For example the misclassified gain weight and those weights which are classified correctly, lose weight. It is kind of the same strategy when dealing with stocks; you balance the investment between bonds and share. An analog could also be done to illnesses; If a doctor informs that you have a rare disease, you want to make sure to get a few more opinions from other doctors, You will evaluate all the information to make a more correct decision about how to cure yourself. Why use gradient boost: - Gradient boost provides the user with a powerful tool to boost/improve weak prediction models. - Gradient boost works well with regression and classification problems, therefore Decision tree can benefit from applying gradient boost. - Gradient bo​ost is known in the industry, to be one of the best techniques to use when dealing with model improvement. - Gradient boost uses stagewise fashion, in this way each time it adjust a tree, it does not go back and readjust when dealing with the next tree. As with all machine learning algorithms gradient boost also have some constraint: - There is a change of overfitting. “Nice to know” tips: - A natural way to reduce this risk of overfitting would be to monitor and adjust the iterations. - The depth of the tree might have an influence on the prediction error, observe what happens if the depth is a stump/1 level deep.

ThingWorx Analytics Interactive API Guide is a great way for users to familiarize themselves with ThingWorx Analytics APIs calls.  It even gives users the ability to run jobs through its interface.  This blog post will cover how to access the ThingWorx Analytics Interactive API Guide installed on a Virtual Machine or Standalone Server. Steps Get the IP address of the ThingWorx Analytics Server Type ​ip a ​Put that IP address into the desired web browser ​Your IP address may be different from the one in the picture above Add the port number of the server to the end of the IP address ​The Default  port number is 8080 Make sure to put a colon " : " between the end of the IP address and the start of the port number The port number could be different in some cases, depending if it was configured differently during installation ​Hit Enter and the main page will load.

This is part of the continuing series of Blog posts regarding Troubleshooting the Application, this article will discuss more advance issues that some clients and customer have encountered while building or using ThingWorx Analytics. Packer Script Error – Unable to Download CentOS Image As the application is developed and built inside a CentOS image, the ThingWorx Analytics Packer Script tool for Virtual Machine Appliance creation utilizes the CentOS mirror repository in the creation process. When the end user is attempting to build the Virtual Machine Appliance with the Packer Script media creation tool, part of the process is to download the CentOS 7 ISO image file as the basis for the operating system that the ThingWorx Analytics Server software will be installed to. If CentOS updates or changes their mirror links for the source file ISO, you may encounter the following error: ==> virtualbox-iso: Downloading or copying Guest additions virtualbox-iso: Downloading or copying: file:///C:/Program%20Files/Oracle/VirtualBox/VBoxGuestAdditions.iso ==> virtualbox-iso: Downloading or copying ISO virtualbox-iso: Downloading or copying: file:///local-file-repo/CentOS-7-x86_64-Minimal-1511.iso virtualbox-iso: Error downloading: open local-file-repo/CentOS-7-x86_64-Minimal-1511.iso: The system cannot find the path specified. virtualbox-iso: Downloading or copying: http://mirror.spro.net/centos/7/isos/x86_64/CentOS-7-x86_64-Minimal-1511.iso virtualbox-iso: Error downloading: checksums didn't match expected: 88c0437f0a14c6e2c94426df9d43cd67 ==> virtualbox-iso: ISO download failed. Build 'virtualbox-iso' errored: ISO download failed. ==> Some builds didn't complete successfully and had errors: --> virtualbox-iso: ISO download failed. ==> Builds finished but no artifacts were created. Solution Method 1: Configuration File Replacement We have created a custom JSON configuration file that resolves the mirror issue for CentOS 7 v1611. You can download the JSON file here; you may have to right-click and “save link as” a JSON extension file. Also note, you will have to save/rename this JSON file as neuron-solo-variables.json. Using this file, navigate to your Packer Script builder directory, usually this is found in the following path: <PATH>\ThingWorx-Analytics-Server-Standalone\components\vm-builder\neuron-vm-builder Copy the new JSON file into this directory, and replace the current existing copy. You can now re-run the Packer Script for your desired Virtual Machine Appliance output. Method 2: Manual Configuration File Adjustment You will have to locate an active mirror for CentOS 7. A list of current active mirrors can be found here. When selecting a mirror, you will need to select the Minimal ISO install, as this is the base image that is used for the VM creation. Next, you will have to open the current neuron-solo-variables.json configuration file located in the <PATH>\ThingWorx-Analytics-Server-Standalone\components\vm-builder\neuron-vm-builder directory. You will have to replace the os_image_download_url value with an active Mirror URL from the list above. Next, for the os_iso_md5_checksum variable, you will need to replace the entry with the new SHA256 checksum from CentOS, which can be located here. Default Settings: New Settings: Save changes and close the neuron-solo-variables.json configuration file. CentOS has switched over from MD5 to SHA256 checksums. Even though in the following the variable name has “MD5” in the string, we will be modifying a second JSON configuration file to address this. In the same directory that we are currently working in, open the neuron-solo.json configuration file. You will need to modify the attribute iso_checksum_type to sha256 Default Settings: New Settings: Save changes and close the neuron-solo.json configuration file. You can now re-run the Packer Script for your desired Virtual Machine Appliance output.

In this video we cover the installation of the UploadThing module. This video applies to ThingWorx Analytics 52.2 till 8.0. This is no longer applicable with ThingWorx Analytics 8.1   Useful links: How to copy files from Windows to Linux Updated Link for access to this video:  Installing Thingworx Analytics Builder: Part 3 of 3  

In this video we cover the different configuration steps required for ThingWorx Analytics Builder extension This video applies to ThingWorx Analytics 52.1 till 8.1.   Note though: - this video uses Classic Composer, the same operations can be done using the New Composer starting with version 8.0 as illustrated in the Help Center - For release 8.1, the Settings menu differs from previous versions, see Video Link : 2079 between times 00:12 sec to 00:40 sec for up to date menu selection.   Updated Link for access to this video:  Installing Thingworx Analytics Builder: Part 2 of 3

This document provides API information for all 51.0 releases of ThingWorx Machine Learning.

Applicable releases: 8.3 and above   Description: In this video, we will see  Basic concepts of SVM Some scenarios for a better understanding

Continuing our series of Troubleshooting ThingWorx Analytics installations, in this IoT Tech Tip we will cover two items have been appearing for many users.   Error 1069 Encountered with Native Windows Installation of ThingWorx Analytics 8.2   In some instances, when a user successfully installs ThingWorx Analytics (TWAS) to a Windows Server operating system, they will encounter an error where TWAS will report an Error 1069: The Service did not start due to logon failure.   This can occur with any individual Service that is created by the installation, the following fix should work in addressing the issue.   Primary Reason This Happens:   This error can be encountered when the user provides incorrect credentials for associating the Services to an account during installation. In TWAS 8.2, there is a utility that will enable to the user to change the associated user on the Services. It is important the user provides the password for the User Account on Windows, and not the user/password combination for ThingWorx Foundation Platform Server.   Steps to Fix Issue   Solution 1:   Open a Command Prompt as Administrator, via Start Menu à Run à type CMD. Then right click on cmd.exe and Run As Administrator.   In the elevated command prompt, change your directory to the ThingWorxAnalyticsServer/bin directory, for example in the default installation path would be: cd C:\Program Files (x86)/ThingWorxAnalyticsServer/bin Then execute the changeServiceUserAccount.bat <username>, for example: changeServiceUserAccount.bat user1   You will be prompted to change the password for the user.   Solution 2:   If Solution 1 does not resolve the issue, alternately you can manually change the Log On properties for each of the services. The changeServiceUserAccount.bat would do this via script, but on occasion this may work. Open the Control Panel and navigate to Services, for example: Control Panel à All Control Panel Items à Administrative Tools   You will have to right click each individual service and go to Properties à Log On tab and enter the account name and password for the local account. Note: Local System account will not resolve this issue.   This issue was resolved in the ThingWorx Analytics Server 8.3 release, where all Services are associated with the Network Service account.     More information can be found in this Knowledge Article   Uploading of a Dataset hangs or does not complete in ThingWorx Analytics 8.3   On occasion, after a fresh installation of ThingWorx Analytics Server 8.3 on a Windows Server operating system, a dataset will not complete its upload. Typically no error message is displayed, and the upload wizard UI will just hang on the upload progress after:   Creating copy of Configuration File... Submitting Create Dataset request... Creating copy of Data File...   Primary Reason This Happens:   This is caused by twas-zookeeper service being stuck in a PAUSED state. This means that in the post installation, twas-zookeeper did not start.   Steps to Fix Issue   You will have to double check that the JAVA_HOME variable was defined as a System Variable. In the ThingWorx Analytics Installation guide, pages 12-14 outline the steps required as pre-requisites. You can change this in Control Panel > System > Advanced Settings > Environment Variables, and ass a new variable named JAVA_HOME under System Variables. The value location should be the location of your deployment of JAVA software.   Typically this is located in C:\Program Files\Java\<jre or jdk>_<version number>     More information can be found in this Knowledge Article