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en:iot-reloaded:introduction_to_time_series_analysis [2024/12/03 17:35] – [A cooling system case] pczekalskien:iot-reloaded:introduction_to_time_series_analysis [2025/05/13 14:59] (current) – [A cooling system case] pczekalski
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 In the context of IoT systems, there might be several reasons why time series analysis is needed.  In the context of IoT systems, there might be several reasons why time series analysis is needed. 
-The most widely ones are the following:+The most widely used ones are the following:
   * **Process dynamics forecasting** for higher-performing decision support systems. An IoT system, coupled with appropriate cloud computing or other computing infrastructure, can provide not only a rich insight into the process dynamics but also a reliable forecast using regression algorithms like the ones discussed in the regressions section or more advanced like autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) ((Hyndman, Rob J; Athanasopoulos, George. 8.9 Seasonal ARIMA models. oTexts. Retrieved 19 May 2015.)) ((Box, George E. P. (2015). Time Series Analysis: Forecasting and Control. WILEY. ISBN 978-1-118-67502-1.)).   * **Process dynamics forecasting** for higher-performing decision support systems. An IoT system, coupled with appropriate cloud computing or other computing infrastructure, can provide not only a rich insight into the process dynamics but also a reliable forecast using regression algorithms like the ones discussed in the regressions section or more advanced like autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) ((Hyndman, Rob J; Athanasopoulos, George. 8.9 Seasonal ARIMA models. oTexts. Retrieved 19 May 2015.)) ((Box, George E. P. (2015). Time Series Analysis: Forecasting and Control. WILEY. ISBN 978-1-118-67502-1.)).
   * **Anomaly detection** is a highly valued feature of IoT systems. In its essence, anomaly detection is a set of methods enabling the recognition of unwanted or abnormal behaviour of the system over a specific time period. Anomalies might be expressed in data differently:   * **Anomaly detection** is a highly valued feature of IoT systems. In its essence, anomaly detection is a set of methods enabling the recognition of unwanted or abnormal behaviour of the system over a specific time period. Anomalies might be expressed in data differently:
-    * **A certain event in time:** for instance, a measurement jumps over a defined threshold value. This is the simplest type of anomaly, and most of the control systems cope with it by setting appropriate threshold values and alerting mechanisms; +    * **A certain event in time:** for instance, a measurement jumps over a defined threshold value. This is the simplest type of anomaly, and most control systems cope with it by setting appropriate threshold values and alerting mechanisms. 
-    * **Change of a data fragment shape:** This might happen to technical systems, where a typical response to control inputs has changed to some shape that is not anticipated or planned. A simple example is an engine’s response to turning it on and reaching typical rpm values. Due to overloads, worn-out mechanics, or other reasons, the response might take too long, signalling that the device has to be repaired. +    * **Change of a data fragment shape:** This might happen to technical systems, where a typical response to control inputs has changed to some shape that is not anticipated or planned. A simple example is an engine's response to turning it on and reaching typical rpm values. Due to overloads, worn-out mechanics, or other reasons, the response might take too long, signalling that the device has to be repaired. 
     * **Event density:** Many technical systems' behaviour is seasonal–cyclic. Changes in the periods and their absolute values, or their response shapes within the period, are excellent predictors of current or future malfunctioning. So, recognition of typical period shapes and response shapes in time is of high value for predictive maintenance, process control, and other applications of IoT systems.      * **Event density:** Many technical systems' behaviour is seasonal–cyclic. Changes in the periods and their absolute values, or their response shapes within the period, are excellent predictors of current or future malfunctioning. So, recognition of typical period shapes and response shapes in time is of high value for predictive maintenance, process control, and other applications of IoT systems. 
-    * **Event value distribution:** In most measuring systems, measurements due to imperfection of sensors or systems are distributed around some actual value, providing an estimate of the true value with some variance. Due to mechanical wear, the variance might increase or change in value distribution over time, a good indicator and predictor of malfunctioning or possible system failures.+    * **Event value distribution:** In most measuring systems, measurements due to imperfection of sensors or systems are distributed around some actual value, providing an estimate of the true value with some variance. Due to mechanical wear, the variance might increase or change in value distribution over time, which is a good indicator and predictor of malfunctioning or possible system failures.
    ===  ===    ===  ===
 Due to its diversity, various algorithms might be used in anomaly detection, including those covered in previous chapters. For instance, clustering for typical response clusters, regression for normal future states estimation and measuring the distance between forecast and actual measurements, and classification to classify normal or abnormal states. An excellent example of using classification-based methods for anomaly detection is Isolation forests ((IsolationForest example — scikit-learn 1.5.2 documentation)) Due to its diversity, various algorithms might be used in anomaly detection, including those covered in previous chapters. For instance, clustering for typical response clusters, regression for normal future states estimation and measuring the distance between forecast and actual measurements, and classification to classify normal or abnormal states. An excellent example of using classification-based methods for anomaly detection is Isolation forests ((IsolationForest example — scikit-learn 1.5.2 documentation))
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-While most of the methods covered here might be employed in time series analysis, this chapter outlines anomaly detection and classification cases through an example of an industrial cooling system.+While most of the methods covered here might be employed in time series analysis, this chapter outlines anomaly detection and classification cases through an industrial cooling system example.
  
 ===== A cooling system case ===== ===== A cooling system case =====
  
-A given industrial cooling system has to maintain a specific temperature mode of around -18oC. Due to the technology specifics, it goes through a defrost cycle every few hours to avoid ice deposits, leading to inefficiency and potential malfunction. However, a relatively short power supply interruption has been noticed at some point, which needs to be recognised in the future for reporting appropriately. The logged data series is depicted in the following figure {{ref>Cooling_system}}:+A given industrial cooling system has to maintain a specific temperature mode of around -18C. Due to the specifics of the technology, it goes through a defrost cycle every few hours to avoid ice deposits, leading to inefficiency and potential malfunction. However, a relatively short power supply interruption has been noticed at some point, which needs to be recognised in the future for reporting appropriately. The logged data series is depicted in the following figure {{ref>Cooling_system}}:
  
  
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 One might notice that: One might notice that:
-  * Samples of different patterns are different in length; +  * Samples of different patterns are different in length. 
-  * Samples of the same pattern are of different lengths; +  * Samples of the same pattern are of different lengths. 
-  * The interested phenomena (spike) are located at different locations within the samples and are slightly different. +  * The interesting phenomena (spikes) are located at different locations within the samples and are slightly different. 
 The abovementioned issues expose the problem of calculating distances from one example to another since comparing data points will produce misleading distance values. To avoid it, a Dynamic Time Warping  (DTW) metric has to be employed ((Gold, Omer; Sharir, Micha (2018). "Dynamic Time Warping and Geometric Edit Distance: Breaking the Quadratic Barrier". ACM Transactions on Algorithms. 14 (4). doi:10.1145/3230734. S2CID 52070903.)). For the practical implementations in Python, it is highly recommended to visit TS learn library documentation ((Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, & Eli Woods (2020). TSlearn, A Machine Learning Toolkit for Time Series Data. Journal of Machine Learning Research, 21(118), 1-6.)). The abovementioned issues expose the problem of calculating distances from one example to another since comparing data points will produce misleading distance values. To avoid it, a Dynamic Time Warping  (DTW) metric has to be employed ((Gold, Omer; Sharir, Micha (2018). "Dynamic Time Warping and Geometric Edit Distance: Breaking the Quadratic Barrier". ACM Transactions on Algorithms. 14 (4). doi:10.1145/3230734. S2CID 52070903.)). For the practical implementations in Python, it is highly recommended to visit TS learn library documentation ((Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, & Eli Woods (2020). TSlearn, A Machine Learning Toolkit for Time Series Data. Journal of Machine Learning Research, 21(118), 1-6.)).
  
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 </figure> </figure>
  
-As it might be noticed, the query (black) samples are somewhat different from the ones found to be "closest" by the KNN. However, because of the DTW advantages, the classification is done perfectly. +As might be noticed, the query (black) samples are somewhat different from the ones found to be "closest" by the KNN. However, because of the DTW advantages, the classification is done perfectly. 
 The same idea demonstrated here might be used for unknown anomalies by setting a similarity threshold for DTW, classifying known anomalies as shown here, or even simple forecasting. The same idea demonstrated here might be used for unknown anomalies by setting a similarity threshold for DTW, classifying known anomalies as shown here, or even simple forecasting.
  
en/iot-reloaded/introduction_to_time_series_analysis.1733247308.txt.gz · Last modified: 2024/12/03 17:35 by pczekalski
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