You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


# Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147. [doi:10.1016/j.neucom.2017.04.070][1] (Open Access paper)

Installing NAB
--------------
All the figures in this paper were generated using [NAB][2] version 1.0. NAB is written in python and it requires [python 2.7][3] to be installed on your system.

To install [NAB][2] and its dependencies, use the following command:

```
git clone https://github.com/numenta/NAB.git
cd NAB
git checkout v1.0
pip install -e .
```


Generating figures
------------------

NAB will install a command line tool called `nab-plot` that can be used to plot specific charts. We will use this tool to generate most of the figures.

```
usage: nab-plot [-h] [--value {value,raw,likelihood}] [--start START]
                [--end END] [--labels] [--no-labels] [--windows] [--probation]
                [--title TITLE] [--xLabel XLABEL] [--no-xLabel]
                [--yLabel YLABEL] [--fontSize FONTSIZE] [--width WIDTH]
                [--height HEIGHT] [--offline] [--output OUTPUT]
                file
```

You can generate all plots at once using the `generate_plots.sh` script or generate each individual figure using the instructions below.

### Figure 1: Real-world temperature sensor data from an internal component of a large industrial machine

The figure shows real-world temperature sensor data from an internal component of a large industrial machine. Anomalies are labeled with circles. The first anomaly was a planned shutdown. The third anomaly was a catastrophic system failure. The second anomaly, a subtle but observable change in the behavior, indicated the actual onset of the problem that led to the eventual system failure. The anomalies were hand-labeled by an engineer working on the machine. This file is included in the [Numenta Anomaly Benchmark corpus][2]
```
nab-plot realKnownCause/machine_temperature_system_failure.csv --offline --no-xLabel --yLabel="Temperature" --start="2013-12-11 06:10:00"
```
![Figure 1](figures/fig1.png)

### Figure 2: CPU utilization (percent) for an Amazon EC2 instance

CPU utilization (percent) for an Amazon EC2 instance (data from the [Numenta Anomaly Benchmark][2]). A modification to the software running on the machine caused the CPU usage to change. The initial anomaly represents a changepoint, and the new system behavior that follows is an example of concept drift. Continuous learning is essential for performing anomaly detection on streaming data like this.
```
nab-plot realAWSCloudwatch/ec2_cpu_utilization_5f5533.csv --offline --no-xLabel --yLabel="CPU Utilization (Percent)" --start="2014-02-23 00:00:00" --end="2014-02-25 23:59:59"
```
![Figure 2](figures/fig2.png)


### Figure 5: An example stream along with prediction error

**Fig 5a**. This plot shows CPU usage on a database server over time. There are two unusual behaviors in this stream, the temporary spike up to 75% and the sustained shift up to 30% usage
```
nab-plot realAWSCloudwatch/rds_cpu_utilization_e47b3b.csv --offline --no-xLabel --yLabel="CPU Utilization (Percent)" --fontSize=14 --width=1000 --height=400
```
![Figure 5a](figures/fig5a.png)

**Fig 5b**. This plot shows the prediction error while the HTM trains on this stream. Early during training the prediction error is high while the HTM model learns the data. There is a spike in prediction error corresponding to the temporary spike in CPU usage that quickly drops once usage goes back near normal. Finally there is an increase in prediction error corresponding to the sustained shift, which drops after the HTM has learned the new behavior.
```
nab-plot realAWSCloudwatch/rds_cpu_utilization_e47b3b.csv --offline --no-xLabel --value="raw" --fontSize=14 --width=1000 --height=400
```
![Figure 5b](figures/fig5b.png)


### Figure 6: A very noisy, unpredictable stream.

**Fig 6a**. The data shows the latency (in seconds) of a load balancer on a production website. The anomaly (indicated by the dot) is an unusual sustained increase in latencies around April 10.
```
nab-plot artificialWithAnomaly/art_load_balancer_spikes.csv --offline --no-xLabel --yLabel="Latency" --fontSize=16 --width=1000 --height=300
```
![Figure 6a](figures/fig6a.png)

**Fig 6b**. The prediction error from an HTM model on the latency values. The unpredictable nature of the latencies results in frequent spikes in the prediction error that cannot be distinguished from the true positives. The fact that the unpredictable metric values are spikes and the rest of the latencies are close to zero results in the coincidental similarity between the latencies and resulting prediction error.
```
nab-plot artificialWithAnomaly/art_load_balancer_spikes.csv --offline --no-xLabel --value="raw" --fontSize=16 --width=1000 --height=300
```
![Figure 6b](figures/fig6b.png)

**Fig 6c**. A log-scale plot of the anomaly likelihood computed from the prediction error. Unlike the prediction error plot, there is a clear peak right around the real anomaly.
```
nab-plot artificialWithAnomaly/art_load_balancer_spikes.csv --offline --no-xLabel --value="likelihood" --fontSize=16 --width=1000 --height=300
```
![Figure 6c](figures/fig6c.png)


### Figure 7: Several data streams from the NAB corpus, showing a variety of data source and characteristics

**Fig 7a**. Click-through prices for online advertisements
```
nab-plot realAdExchange/exchange-4_cpc_results.csv --offline --no-xLabel --yLabel="Cost Per Click" --fontSize=24
```
![Figure 7a](figures/fig7a.png)

**Fig 7b**. An artificial stream with some noise but no anomalies
```
nab-plot artificialNoAnomaly/art_daily_small_noise.csv --offline --no-xLabel --yLabel="Metric" --fontSize=24
```
![Figure 7b](figures/fig7b.png)

**Fig 7c**. AWS Cloudwatch CPU utilization data
```
nab-plot realAWSCloudwatch/ec2_cpu_utilization_fe7f93.csv --offline --no-xLabel --yLabel="CPU Utilization (Percent)" --fontSize=24
```
![Figure 7c](figures/fig7c.png)

**Fig 7d**. Autoscaling group data for a server cluster
```
nab-plot realAWSCloudwatch/grok_asg_anomaly.csv --offline --no-xLabel --yLabel="Autoscaling Group Size" --fontSize=24
```
![Figure 7d](figures/fig7d.png)

**Fig 7e**. A stream of tweet volumes related to FB stock
```
nab-plot realTweets/Twitter_volume_FB.csv --offline --no-xLabel --yLabel="Tweets Referencing Facebook" --fontSize=24
```
![Figure 7e](figures/fig7e.png)

**Fig 7f**. Hourly demand for New York City taxis
```
nab-plot realKnownCause/nyc_taxi.csv --offline --no-xLabel --yLabel="NYC Taxi Demand" --fontSize=24
```
![Figure 7f](figures/fig7f.png)


### Figure 8: A zoomed in view showing the anomaly window around the first anomaly in Fig. 1.

A zoomed in view showing the anomaly window around the first anomaly in Fig. 1. The window size is set large enough to reward early detections of the anomaly, yet small enough such that poor detections count as false positives.
```
nab-plot realKnownCause/machine_temperature_system_failure.csv --offline --no-xLabel --yLabel="Temperature" --windows --start="2013-12-13 05:20:00" --end="2013-12-20 08:50:00"
```
![Figure 8](figures/fig8.png)


### Figure 9: Detector results for two example NAB data streams

These plots show detector results for two example NAB data streams. In both cases we show a subset of the full data stream. The plotted shapes correspond to the detections of seven different detectors: HTM, Multinomial Relative Entropy, Twitter ADVec, Skyline, Sliding Threshold, Bayesian Online Changepoint, and EXPoSE. Shapes that correspond to the same data point have been spaced vertically for clarity. For a given detector, true positive detections within each window (red shaded regions) are labeled in black. All false positive detections are colored red. (a) Detection results for a production server's CPU metric. The second anomaly shows a sustained shift that requires algorithms to adjust to a new normal behavior. (b) Results for the data stream shown in Fig. 2. Here we see a subtle temporal anomaly that preceded a large, obvious spike in the data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

```
python fig9.py
```

![Figure 9](figures/fig9.png)


[1]: https://doi.org/10.1016/j.neucom.2017.04.070
[2]: https://github.com/numenta/NAB/tree/v1.0
[3]: https://www.python.org/downloads