Time-series Analysis in Minutes
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The document discusses time-series analysis, outlining its definition, components, and various applications, including classification, prediction, and anomaly detection. It covers exploratory analytics using R for visualization and data management tools suited for large datasets. Additionally, it presents predictive analytics techniques such as exponential smoothing and ARIMA models, along with the use of neural networks for improved forecasting and anomaly detection.
Time-series Analysis in Minutes
- 1. Time-Series Analysis in Minutes Founder & CEO Shanti Subramanyam Orzota, Inc. 1
- 2. Agenda § Time-series Introduction › The What and Why: Use Cases › Why Analyze § Exploratory Analytics › Fast Visualization › Insights › Predictive Analytics › Forecasting › Advanced Prediction 2
- 4. Type of Data Use Case Financial Data Analyze stock performance Census Data Analyze population types and growth Sales Data Analyze sales by region, product, etc. Industrial Data Analyze machine performance Sample Uses Cases
- 5. Introduction • Time series analytics in a variety of applications • Classification • Prediction • Anomaly detection • Pattern discovery • And more… 5 Pattern 1 Pattern 2 Introduction Hybrid Neural Network(HNN) TreNet for Local Trend ntroduction Time series analytics in a variety of applications • Classification • Prediction • Anomaly detection • Pattern discovery • And more… 5 Pattern 1 Pattern 2 ntroduction Hybrid Neural Network(HNN) TreNet for Local Trend roduction me series analytics in a variety of applications Classification Prediction Anomaly detection Pattern discovery And more… 5 Pattern 1 Pattern 2 duction Hybrid Neural Network(HNN) TreNet for Local Trend oduction me series analytics in a variety of applications Classification Prediction Anomaly detection Pattern discovery And more… 5 Pattern 1 Pattern 2 uction Hybrid Neural Network(HNN) TreNet for Local Trend Time Series Applications Classification Prediction Anomaly Detection Pattern Discovery
- 6. Time Series Components • Trend Component • Seasonal Component • Cyclic Component • Irregular Fluctuations
- 7. Time Series Components • Trend – gradual upward or downward shift • Seasonality – repeating pattern at fixed intervals of time within a year
- 11. Exploratory Analysis • Basic analysis using R Load data from csv files dat <- read.csv(“/path/to/file.csv”) • Create time-series timeseries <- ts(dat, start=c(1987,1), frequency=12) plot.ts(timeseries)
- 19. Data Management • Storing large amounts of historical time series data requires a scalable platform • TimeScale DB • InfluxDB • OpenTSDB (built on HBase) • Riak TS • Things to look for: • Data Ingestion Rate • Query Speed and Capability • Scalability • Reliability
- 25. Predictions § Forecast Time-Series Trend › Sales Forecasts have been done for decades › Sensor Data Forecasts can help fix issues before it is too late › Anomaly Detection › Network Security use cases: DDOS attacks, failed logins › Predictive Maintenance › Usually requires more than just Time Series prediction 25
- 26. Exponential Smoothing § Single Exponential Smoothing § Use when there is no clear trend or seasonal pattern § Works for slow trend changes as well 26
- 31. Time Series in Spark Spark-TS • Analyze large amounts of TS data • In Scala, Java OR Python • Functionality similar to R • Exploratory Analysis • Save the data in Hive and use standard tools like Tableau • Modeling and Predictions • Exponential, ARIMA, other Auto-regressive models as well
- 32. Predictions Using Neural Networks § Recurrent Neural Networks § Memorize previous info and apply it to current output § Complexity reduction ècurrent state relates only to few previous states § LSTM – Long Short Term Memory § Widely Used Variant of RNNs § Model learns based on the outputs § Uses memory units to learn § when to “forget” or “update” info § Ref: http://file.scirp.org/pdf/JDAIP_2017082815492555.pdf 32
- 33. LSTM GNB for Anomaly Detection § LSTM Gaussian Naïve Bayes Model § Enhances LSTM using Gaussian Naïve Bayes Model § Uses predicted error from the LSTM network to construct Gauss-NBayes § Detects anomalies from the errors and learns from it 33
- 34. Conclusion • Time Series Data is Every Where • Exploratory Analysis can provide lots of useful insights • Trend, Seasonality, Cyclic Components • Single Series Analysis • Multiple Related Series • Aggregation of Similar Series • Predictive Analytics • Forecasting • Anomaly Detection • Predictive Maintenance
- 35. Additional Info