The explosion of sensors in all types of devices from “smart” consumer wearables and appliances to complex machines on manufacturing floors has given rise to a requirement to quickly analyze vast quantities of sensor metrics to provide meaningful insights. From exploratory to predictive analytics, analyzing time-series data is essential to address inefficiencies, identify risks and improve operations. In this presentation, we will see how you can conduct exploratory analytics of time-series data rapidly to gain insights into the performance of the machines being monitored. We will talk about how to look at data from multiple metrics together in a holistic way to hone in on anomalies and identify potential problems. Finally, we will cover algorithms and techniques to predict future trends for time-series metrics. Along the way, we will discuss useful tools and technologies to perform time-series data analysis in minutes.

1. Time-Series Analysis in Minutes
Founder & CEO
Shanti Subramanyam
Orzota, Inc.
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2. Agenda
§ Time-series Introduction
› The What and Why: Use Cases
› Why Analyze
§ Exploratory Analytics
› Fast Visualization
› Insights
› Predictive Analytics
› Forecasting
› Advanced Prediction
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3. Time Series Definition
• Series of data points listed in time order
• Commonly taken at successive, equally spaced points in time

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…
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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

8. Cyclic Component
Time Series that exhibits rises and falls or some other pattern that are
NOT of a fixed period is said to have a cyclic pattern.
Typically this is exhibited over multiple years.
Example:
Agri Commodities exhibit cyclical
component.
Area under cultivation increases
when there is a price increase in
the market due to high demand
and contracts when the price
starts falling with a lag

9. Time Series Components

10. Exploratory Analysis
Code and data samples available at:
https://github.com/Orzota/tutorials/tree/master/Time-Series

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)

12. TSstudio package
• Much better visualization and processing for TS data
install.packages("TSstudio")
install.packages("xts")
library(TSstudio)
library(xts)
ts_plot(timeseries)

13. Seasonality Plot
ts_seasonal (timeseries, type=“normal”)

14. Seasonality Plot
ts_seasonal (timeseries, type=“cycle”)

15. Seasonality Plot
ts_seasonal (timeseries, type=“box”)

16. Heatmaps
ts_heatmap (timeseries)

17. 3D Surface Plots
ts_surface (timeseries)

18. Multiple Time Series

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

20. Aggregated Analytics
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21. Aggregated Analytics

22. Aggregated Analytics

23. Aggregated Analytics
One model type has high
“Pending Sector Count”

24. Predictive Analytics
Code and data samples available at:
https://github.com/Orzota/tutorials/tree/master/Time-Series

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
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26. Exponential Smoothing
§ Single Exponential Smoothing
§ Use when there is no clear
trend or seasonal pattern
§ Works for slow trend changes
as well
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27. Exponential Smoothing
§ Double Exponential Smoothing (Holt’s Method)
§ Use when there is a clear trend
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28. Triple Exponential Smoothing
§ Also known as Holt-
Winters Method
§ Exponential Smoothing
applied to seasonality,
level and trend
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29. Forecasting in R
ARIMA Model
• Great for short-term forecasting; can handle seasonality, trends,
errors, cycles, etc.
• Cannot handle anomalies

30. Forecasting Time Series

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
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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
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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