The Splunk Machine Learning Toolkit is packed with machine learning algorithms, new visualizations, web assistant and much more. This blog sheds light on some features and commands in Splunk Machine Learning Toolkit (MLTK) or Core Splunk Enterprise that are lesser known and will assist you in various steps of your model creation or development. With each new release of the Splunk or Splunk MLTK a catalog of new commands are available. I attempt to highlight commands that have helped in some data science or analytical use-cases in this blog.

https://discoveredintelligence.com/wp-content/uploads/2020/10/image-10.png2561892Discovered Intelligencehttps://discoveredintelligence.com/wp-content/uploads/2013/12/DI-Logo1-300x137.pngDiscovered Intelligence2020-11-12 20:24:382022-10-31 14:41:21Interesting Splunk MLTK Features for Machine Learning (ML) Development

There are multiple (almost discretely infinite) methods of outlier detection. In this blog I will highlight a few common and simple methods that do not require Splunk MLTK (Machine Learning Toolkit) and discuss visuals (that require the MLTK) that will complement presentation of outliers in any scenario. This blog will cover the widely accepted method of using averages and standard deviation for outlier detection. The visual aspect of detecting outliers using averages and standard deviation as a basis will be elevated by comparing the timeline visual against the custom Outliers Chart and a custom Splunk’s Punchcard Visual.

Some Key Concepts

Understanding some key concepts are essentials to any Outlier Detection framework. Before we jump into Splunk SPL (Search Processing Language) there are basic ‘Need-to-know’ Math terminologies and definitions we need to highlight:

Outlier Detection Definition: Outlier detection is a method of finding events or data that are different from the norm.

Average: Central value in set of data.

Standard Deviation: Measure of spread of data. The higher the Standard Deviation the larger the difference between data points. We will use the concept of standard substantially in today’s blog. To view the manual method of standard deviation calculation click here.

Time Series: Data ingested in regular intervals of time. Data ingested in Splunk with a timestamp and by using the correct ‘props.conf’ can be considered “Time Series” data

Additionally, we will leverage aggregate and statistic Splunk commands in this blog. The 4 important commands to remember are:

Bin: The ‘bin’ command puts numeric values (including time) into buckets. Subsequently the ‘timechart’ and ‘chart’ function use the bin command under the hood

Eventstats: Generates statistics (such as avg,max etc) and adds them in a new field. It is great for generating statistics on ‘ALL’ events

Streamstats: Similar to ‘stats’ , streamstats calculates statistics at the time the event is seen (as the name implies). This feature is undoubtedly useful to calculate ‘Moving Average’ in additional to ordering events

Stats: Calculates Aggregate Statistics such as count, distinct count, sum, avg over all the data points in a particular field(s)

Data Requirements

The data used in this blog is Splunk’s open sourced “Bots 2.0” dataset from 2017. To gain access to this data please click here. Downloading this data set is not important, any sample time series data that we would like to measure for outliers is valid for the purposes of this blog. For instance, we could measure outliers in megabytes going out of a network OR # of logins in a applications using the using the same type of Splunk query. The logic used to the determine outliers is highly reusable.

Using SPL

There are four methods commonly seen methods applied in the industry for basic outlier detection. They are in the sections below:

1. Using Static Values

The first commonly used method of determining an outlier is by constructing a flat threshold line. This is achieved by creating a static value and then using logic to determine if the value is above or below the threshold. The Splunk query to create this threshold is below :

In addition to using arbitrary static value another method commonly used method of determining outliers, is a multiplier of the average. We calculate this by first calculating the average of your data, following by selecting a multiplier. This creates an upper boundary for your data. The Splunk query to create this threshold is below:

<your spl base search> …
| timechart span=12h sum(mb_out) as mb_out
| eventstats avg("mb_out") as average
| eval threshold=average*2
| eval isOutlier=if('mb_out' > threshold, 1, 0)

3. Average with Standard Deviation

Similar to the previous methods, now we use a multiplier of standard deviation to calculate outliers. This will result in a fixed upper and lower boundary for the duration of the timespan selected. The Splunk query to create this threshold is below:

<your spl base search> ... | timechart span=12h sum(mb_out) as mb_out
| eventstats avg("mb_out") as avg stdev("mb_out") as stdev
| eval lowerBound=(avg-stdev*exact(2)), upperBound=(avg+stdev*exact(2))
| eval isOutlier=if('mb_out' < lowerBound OR 'mb_out' > upperBound, 1, 0)

Notice that with the addition of the lower and upper boundary lines the timeline chart becomes cluttered.

4. Moving Averages with Standard Deviation

In contrast to the previous methods, the 4th most common method seen is by calculating moving average. In short, we calculate the average of data points in groups and move in increments to calculate an average for the next group. Therefore, the resulting boundaries will be dynamic. The Splunk search to calculate this is below:

<your spl base search> ... | timechart span=12h sum(mb_out) as mb_out
| streamstats window=5 current=true avg("mb_out") as avg stdev("mb_out") as stdev
| eval lowerBound=(avg-stdevexact(2)), upperBound=(avg+stdevexact(2))
| eval isOutlier=if('mb_out' < lowerBound OR 'mb_out' > upperBound, 1, 0)

Tips: Notice the “isOutliers” line in the timeline chart, in order to make smaller values more visible format the visual by changing the scale from linear to log format.

Using the MLTK Outlier Visualization

Splunk’s Machine Learning Toolkit (MLTK) contains many custom visualization that we can use to represent data in a meaningful way. Information on all MLTK visuals detailed in Splunk Docs. We will look specifically at the ‘Outliers Chart’. At the minimum the outlier chart requires 3 additional fields on top of your ‘_time’ & ‘field_value’. First, would need to create a binary field ‘isOutlier’ which carries the value of 1 or 0, indicating if the data point is an outlier or not. The second and third field are ‘lowerBound’ & ‘upperBound’ indicating the upper and lower thresholds of your data. Because the outliers chart trims down your data by displaying only the value of data point and your thresholds, we can conclude through use that it is clearer and easier to understand manner. As a recommendation it should be incorporated in your outliers detection analytics and visuals when available.

Continuing from the previous paragraph, take a look at the below snippets at how the impact the outliers chart is in comparison to the timeline chart. We re-created the same SPL but instead of applying timeline visual applied the ‘Outliers Chart’ in the same order:

Advantages

Disadvantages

Cleaner presentation and less clutter

You need to install Splunk MLTK (and its pre-requisites) to take advantage of the outliers chart

Easier to understand as determining the boundaries becomes intuitive vs figuring out which line is the upper or lower threshold

Unable to append additional fields in the Outliers chart

Adding Depth to your Outlier Detection

Determining the best technique of outlier detection can become a cumbersome task. Hence, having the right tools and knowledge will free up time for a Splunk Engineer to focus on other activities. Creating static thresholds over time for the past 24hrs, 7 days, 30 days may not be the best approach to finding outliers. A different way to measure outliers could be by looking at the trend on every Monday for the past month or 12 noon everyday for the past 30 days. We accomplish this by using two simple and useful eval functions:

Continuing from the previous section, we incorporate the two highlighted eval functions in our SPL to calculate the average ‘mb_out’. However, this time the average is based on the day of the week and the hour of the day. There are a handful of advantages of this method:

Extra depth of analysis by adding 2 additional fields you can split the data by

Intuitive method of understanding trends

Some use cases of using the eval functions are as follows:

Network activity analysis

User behaviour analysis

Visualizing the Data!

We will focus on two visualizations to complement our analysis when utilizing the eval functions. The first visual, discussed before, is the ‘Outliers Chart’ which is a custom visualization in Splunk MLTK. The second visual is another custom visualization ‘PunchCard’, it can be downloaded from Splunkbase here (https://splunkbase.splunk.com/app/3129/).

The outliers chart has a feature which results in a ‘swim lane’ view of a selected field/dimension and your data points while highlighting points that are outliers. To take advantage of this feature, we will use a Macro “splitby” which creates a hidden field(s) “_<Field(s) you want data to split by>”. The rest of the SPL is shown below

This search results in an Outlier Chart that looks like this:

The Outliers Chart has the capability to split by multiple fields, however in our example splitting it by a single dimension “HourOfDay” is sufficient to show its usefulness.

The PunchCard visual is the second feature we will use to visualize outliers. It displays cyclical trends in our data by representing aggregated values of your data points over two dimensions or fields. In our example, I’ve calculated the sum of outliers over a month based on “DayOfWeek” as my first dimension and “HourOfDay” as my second dimension. I’ve adding the outliers of these two fields and displaying it using the PunchCart visual. The SPL and image for this visual is show below:

< your base SPL search > ... | streamstats window=10 current=true avg("mb_out") as avg stdev("mb_out") as stdev by "DayOfWeek" "HourOfDay"
| eval avg=round(avg,2)
| eval stdev=round(stdev,4)
| eval lowerBound=(avg-stdevexact(2)), upperBound=(avg+stdevexact(2))
| eval isOutlier=if('mb_out' < lowerBound OR 'mb_out' > upperBound, 1, 0)
| splitby("DayOfWeek","HourOfDay")
| stats sum(isOutlier) as mb_out by DayOfWeek HourOfDay
| table HourOfDay DayOfWeek mb_out

Summary and Wrap Up

Trying to find outliers using Machine Learning techniques can be a daunting task. However I hope that this blog gives an introduction on how you can accomplish that without using advanced algorithms. Consequently, using basic SPL and built-in statistic functions can result in visuals and analysis that is easier for stakeholders to understand and for the analyst to explain. So summarizing what we have learnt so far:

One solution does not fit all. There are multiple methods of visualizing your analysis and exploring your result through different visual features should be encouraged

Use Eval functions to calculate “DayOfWeek” and “HourOfDay” wherever and whenever possible. Adding these two functions provides a simple yet powerful tool for the analyst to explore the data with additional depth

Trim or minimize the noise in your Outliers visual by using the Outliers Chart. The chart is beneficial in displaying only your boundaries and outliers in your data while shaving all other unnecessary lines

Use “log” scale over “linear” scale when displaying data with extremely large ranges

Part II of the Forecasting Time Series blog provides a step by step guide for fitting an ARIMA model using Splunk’s Machine Learning Toolkit. ARIMA models can be used in a variety of business use cases. Here are a few examples of where we can use them:

Detecting anomalies and their impact on the data

Predicting seasonal patterns in sales/revenue

Streamline short-term forecasting by determine confidence intervals

From Part 1 of the blog series, we identified how you can use Kalman Filter for forecasting. The observation we made from the resulting graphs demonstrated how it was also useful in reducing/filtering noise (which is how it gets its name ‘Filter’) . On the other hand ARIMA belongs to a different class of models. In comparison to a Kalman filter, ARIMA models works on data that has moving averages over time or where the value of a data point is linearly depending on its previous value(s). In these two scenarios it makes more sense to use ARIMA over Kalman Filter. However good judgement, understanding of the data-set and objective of forecasting should always be the primary method of determining the algorithm.

Objective

Part II of this blog series aims to familiarize a Splunk user using the MLTK Assistant for forecasting their time series data, particularly with the ARIMA option. This blog is intended as a guide in determining the parameters and steps to utilize ARIMA for your data. In fact, it is a generalized template that can be used with any processed data to forecasting with ARIMA in Splunk’s MLTK. An advantage of using Splunk for forecasting is its benefit in observing the raw data side by side with the predicted data and once the analysis is complete, a user can create alerts or other actions based on a future prediction. We will talk more about creating alerts based on predicted or forecasted data in a future blog (see what I predicted there ;)?)

If you have read part I of our blog, we will reuse the same dataset
process_time.csv for this part. If not, click here to navigate to part I to understand the dataset.

Fundamental Concept for ARIMA Forecasting

A fundamental concept to understand before we move ahead with ARIMA is that the model works best with stationary data. Stationary data has a constant trend that does not change overtime. The average value is also independent of time as another characteristic of stationary data.

A simple example of non-stationary data is are the two graphs below, the first without a trendline, the second with a yellow trendline to show an average increase in the value of our data points. The data needs to be transformed into stationary data to remove the increasing trend.

Using Splunk’s autoregress command we can apply differencing to our data. The results are immediately visible through line chart visual! The below command can be used on any time series data set to demonstrate differencing.

Without creating a trendline for the below graph we can see that the data fluctuates around a constant mean value of ‘0’, we can say that differencing is applied. Differencing to make the data stationary can increase the accuracy and fit of our ARIMA forecast. To read more about differencing and other rules that apply on ARIMA, navigate to the Duke URL provided in the useful link section:

Differencing is simply subtracting the current and previous data points. In our example we are only applying differencing by an order of 1, meaning we will subtract the present data point by one data point in reverse chronological order. There are different types of non-stationary graphs, which require in-depth domain knowledge of ARIMA, however we simplify it in this blog and use differencing to remove the non-constant trend in this example 😊!

From part 1 of this blog series we can see that our data does not have a constant trend, as a result we apply differencing to our dataset. The step to apply differencing from the MLTK Assistant is detailed in the ‘Determining Starting Points’ section. Differencing in ARIMA allows the user to see spikes or drops (outliers) in a different perspective in comparison to Kalman Filter.

Walkthrough of MLTK Assistant for ARIMA

ARIMA is a popular and robust method of forecasting long-term data. From blog 1 we can describe Kalman Filter’s forecasting capabilities as extending the existing pattern/spikes, sort of a copy-paste method which may be advantageous when forecasting short-term data. ARIMA has an advantage in predicting data points when the we are uncertain about the future trend of the data points in the long-term. Now that we have got you excited about ARIMA, lets see how we can use it in Splunk’s MLTK!

We use the Machine Learning Toolkit Assistant for forecasting timeseries data in Splunk. Navigate to the Forecast Time Series Assistant page (Under the Classic Menu option) and use the Splunk ‘inputlookup’ command to view the process_time.csv file.

|inputlookup process_time.csv

Once we add the dataset click on Algorithm and select ‘ARIMA’ (Autoregressive Integrated Moving Average), and ‘value’ as your field to forecast. You will notice that the ARIMA arguments will appear.

There are three arguments that make up the ARIMA model:

Argument

Definition

AutoRegressive – p

Auto regressive (AR) component refers to the use of past values in the regression equation. Higher the value the more past terms you will use in the equation. This concept is also called ‘lags’. Another way of describing this concept is if the value your data point is depending on its previous value e.g process time right now will depend on the process time 30 seconds before (from our data set)

Integrated – d

The d represents the degrees of differencing as discussed in the previous section. This makes up the integrated component of the ARIMA model and is needed for the stationary assumption of the data.

Moving Average – q

Moving Average in ARIMA refers to the use of past errors in the equation. It is the use of lagging (like AR) but for the error terms.

Determine Starting Points

Identify the Order of Differencing (d)

As a refresher, we utilized the same dataset we worked with in part 1 of the blog series regarding the Kalman filter. As I input my process_time.csv file in the assistant, I enter the future_timespan variable as 20 and the holdback as 20. I’ve kept the confidence interval as default value ‘95’. Once the argument values are populated click on ‘Forecast’ to see the resulting graphs.

As a note, my ARIMA arguments described above are ARIMA(0,0,0) which can represented as a mathematics function ARIMA(p,d,q), where p,d,q = 0. We use this functional representation of the variables frequently in this blog for consistently with generally used mathematical languages.

When we click on forecast, observe the line chart graph from the results that show. This above graph confirms that the data is non-stationary, we will apply differencing to make it stationary. We can accomplish this by increasing the value of our ‘d’ argument from ‘0’ to ‘1’ in the forecasting assistant and clicking on forecast again. This step is essential to meet one of the main criteria’s of using ARIMA discussed in the ‘Fundamental Concept for ARIMA’ section.

Identifying AR(p) and MA(q)

After we apply differencing to our data our next step is to determine the AR or MR terms that mitigate any auto correlation in our data. There are two popular methods of estimating the these two parameters. We will expand on one of the methods in this blog.

Method 1

The first method for estimating the value of ‘p’ and ‘q’ is to use the Akaiki Information Criteria (AIC) and the Baysian Information Criteria (BIC), however using them is outside the scope of the blog as we will use a different method from the MLTK given the tools we have at hand. For the curious mind, the following blog contains detailed information on AIC and BIC to determine our ‘p’ and ‘q’ values:

Method 2

After we have applied differencing to our time series data, we review the PCAF and the ACF plots to determine an order for AR(q) or MA(q). We will apply ARIMA(0,1,0) in our ARIMA MLTK assistant and then click on ‘Forecast’ to view the results of the graph. The below image shows the values that we entered in the assistant:

Once we click on forecast, we view the PACF plot to estimate a value for AR(p) model. Similarly we use the ACF plot to estimate a value for MA(q). The graphs are shown in the screenshot below.

We examine the PACF plot for a suggestion for our AR value, by counting the prominent high spikes. From the plot below I’ve circled the prominent spikes in the PACF graph. The value of AR (p) that we pick is 4.

We examine the ACF plot for a suggestion for our MA value, by counting the prominent high spikes. From the plot below I’ve circled the prominent spikes in the ACF graph. The value of AR (q) that we pick is 5.

We can now add in the values for the parameter integrated (d) – 1 and our estimates for AR – 4, and MA -5 in the Splunk MLTK. Once added in the assistant, click on ‘Forecast’.

For this particular combination for values we can see that once we click on ‘Forecast’, we get an error regarding the ‘invertability’ of the dataset as shown in the screenshot below. Without going too deep into the mathematics, it means that our model does not converge when it forecasts. I’ve added a link in the references and links section at the end for your interest! This error can be resolved by adjusting the values of model, similar to a ‘trail an error’ approach explained in the next section.

Optimize Your P and Q Values

Estimating this method of AR and MA is subjective to what can be considered as ‘prominent spikes’, this can result in estimating values of ‘q’ and ‘p’ that are not an optimal fit for the data. To resolve this we constructed a table displaying the R-squared and Root Mean Square Error (RMSE) values from the model error statistics from the MLTK assistance, for each combination of ‘p’ and ‘q’. An empty cell indicates an invertability error, while the other cells contain the value of R-squared and RMSE.

A higher R-squared indicates a better fit the model has on the data. R-squared is the amount of variability that the model can explain on the process time data points.

On the other hand, the lower the RMSE is the better the fit of the model. Root mean square is the difference between the data points the model predicted and our holdback points from the raw data.

We pick values of ‘p’ and ‘q’ that minimize RMSE and maximize R-square as the best fit to our data. From the table below we can see that q=5 and p=5 optimize the prediction for us.

Integrated (d) = 0

AutoRegressive (p)

0

1

2

3

4

5

Moving Average (q)

0

R2 Stat: -0.0015
RMSE: 19.31

R2 Stat: 0.1976
RMSE: 16.35

R2 Stat: 0.1977
RMSE: 16.34

R2 Stat: 0.2699
RMSE: 15.60

R2 Stat: 0.2696
RMSE: 15.60

R2 Stat: 0.3114
RMSE: 15.14

1

R2 Stat: 0.2401
RMSE: 15.91

R2 Stat: 0.2486
RMSE: 15.82

R2 Stat: 0.2780
RMSE: 15.51

R2 Stat: 0.2329
RMSE: 15.98

–

R2 Stat: 0.4053
RMSE: 14.07

2

R2 Stat: 0.2452
RMSE: 15.85

–

–

R2 Stat: 0.3017
RMSE: 15.25

R2 Stat: 0.3214
RMSE: 15.03

–

3

R2 Stat: 0.2872
RMSE: 15.41

R2 Stat: 0.4185
RMSE: 13.92

R2 Stat: 0.4428
RMSE: 13.62

R2 Stat:
RMSE:

R2 Stat: 0.4343
RMSE: 13.72

R2 Stat: 0.4456
RMSE: 13.58

4

R2 Stat: 02826
RMSE: 15.46

R2 Stat: 0.4185
RMSE: 13.92

R2 Stat:0.3241
RMSE: 15.00

–

–

–

5

R2 Stat: 0.2826
RMSE: 15.46

R2 Stat: 0.3133
RMSE: 15.99

R2 Stat: 0.4385
RMSE: 13.67

–

–

R2 Stat: 0.4515
RMSE: 13.52

Viewing Your Results

Once we have picked the values of p and q that optimize our model, we can go ahead plug the numbers in our assistant and click on forecast to display the forecasted graph. The values to plug in the assistant are as follows: p-5, d-1, q-5, holdback-20, forecast-20. The screenshots below show the values entered in the assistant and the resulting forecast graph.

A this point many would be satisfied with the forecast as the visual of the data itself is enough to analyse, asses and then make a judgement on the action(s) to take. The next step details how you can view the data and lists some ideas of alerts that can be constructed

Next Step

We can view the SPL used powering the graph by either clicking on ‘Open in Search’ or ‘ ‘Show SPL’. I prefer the ‘Open in Search’ option as it automatically open a new tab, allowing me to further understand how the SPL is constructed in the forecast and to view the data. Once a tab browser tab opens click on the ‘statistics’ option to view the raw data points, predicted data points and the confidence intervals created by our model. I have added the SPL from the image for your convenience below:

| inputlookup process_time.csv | fit ARIMA _time value holdback=20 conf_interval=95 order=5-1-5 forecast_k=40 as prediction | `forecastviz(40, 20, "value", 95)`

I added another filter to my SPL to only view the forecasted process data from the ARIMA model as shown below:

| inputlookup process_time.csv | fit ARIMA _time value holdback=20 conf_interval=95 order=5-1-5 forecast_k=40 as prediction | `forecastviz(40, 20, "value", 95)` | search "lower95(prediction)"=*

The resulting table lists all the necessary data in a clean tabular format (that we are all familiar with) for creating alerts based on our predicted process time. Here are some ideas on creating alerts based on the data we worked with:

Create alert when the predicted value of the process time goes above a certain threshold

Create alert when the average process time over a timespan is predict to stay above normal limits

Create alert based on outlier detection, when the predicted data is outside the lower or upper boundaries

Creating alerts based on our predict data allows us to be proactive of potential increase or decrease of our input variable

Summarizing ARIMA Forecasting in MLTK

Lets summarize what we have discussed so far in this blog:

A mathematical prerequisites of the model

Determining differencing requirement

Determine starting values for AR() and MA()

Optimize your AR() and MA() values based on error statistics

Forecast your data based on values decided in Step 4

View data and determine any alerts conditions

Prior to the above steps, we need to ensure that our data has been pre-processed or transformed in a MLTK-friendly manner. The pre-process steps include but not limited to; ensuring no gaps in the time series data, determine the relevance of data to forecasting, group data in time intervals (30 second, 1 minute etc). The pre-processing steps are important to create uniformity in the data input allow Splunk’s MLTK to analyse and forecast your data.

Hopefully this blog, streamlines the process of forecasting using ARIMA in Splunk’s MLTK. There are limitations as with any algorithm on forecasting using this method, as it involves a more theoretical knowledge in mathematics I’ve added two links in the the useful links section (first link is navigates you to on ‘datascienceplus.com’ and the second to ’emeraldinsight.com’) to further read on them.

https://discoveredintelligence.com/wp-content/uploads/2018/10/image-14.jpg5341823Discovered Intelligencehttps://discoveredintelligence.com/wp-content/uploads/2013/12/DI-Logo1-300x137.pngDiscovered Intelligence2019-05-06 12:21:352022-10-31 15:35:39Forecasting Time Series Data Using Splunk Machine Learning Toolkit – Part II

In this blog we will use a classification approach for predicting Spam messages. A classification approach categorizes your observations/events in discrete groups which explain the relationship between explanatory and dependent variables which are your field(s) to predict. Some examples of where you can apply classification in business projects are: categorizing claims to identify fraudulent behaviour, predicting best retail location for new stores, pattern recognition and predicting spam messages via email or text. Read more

https://discoveredintelligence.com/wp-content/uploads/2018/10/spam_analytics.jpg346346Discovered Intelligencehttps://discoveredintelligence.com/wp-content/uploads/2013/12/DI-Logo1-300x137.pngDiscovered Intelligence2018-10-16 17:44:082022-11-02 13:54:47Predict Spam Using Machine Learning Classification

The Splunk Machine Learning Toolkit (MLTK) assists in applying machine learning techniques and methods against your data. This article discusses how to get started with the MLTK including installation and some initial testing and examples. Read more

https://discoveredintelligence.com/wp-content/uploads/2017/11/Picture2.png538975Discovered Intelligencehttps://discoveredintelligence.com/wp-content/uploads/2013/12/DI-Logo1-300x137.pngDiscovered Intelligence2018-01-02 17:20:272022-11-04 15:12:41Getting Started With Splunk’s Machine Learning Toolkit