<p align="center">
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<h2 align="center">
Nature-Inspired Algorithms for Time Series Numerical Association Rule Mining
</h2>
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<p align="center">
<a href="#-features">β¨ Features</a> β’
<a href="#-installation">π¦ Installation</a> β’
<a href="#-basic-example">π Basic example</a> β’
<a href="#-reference-papers">π Reference Papers</a> β’
<a href="#-license">π License</a> β’
<a href="#-cite-us">π Cite us</a>
</p>
This framework is designed for **numerical association rule mining in time series data** using **stochastic population-based nature-inspired algorithms**[^1]. It provides tools to extract association rules from time series datasets while incorporating key metrics such as **support**, **confidence**, **inclusion**, and **amplitude**. Although independent from the NiaARM framework, this software can be viewed as an extension, with additional support for time series numerical association rule mining.
[^1]: Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). [A brief review of nature-inspired algorithms for optimization](https://arxiv.org/abs/1307.4186). arXiv preprint arXiv:1307.4186.
* **Free software:** MIT license
* **Python**: 3.11, 3.12
* **Documentation**: [https://niaarmts.readthedocs.io](https://niaarmts.readthedocs.io)
## β¨ Features
The current version of the framework supports two types of time series numerical association rule mining:
- **Fixed Interval Time Series Numerical Association Rule Mining**
- **Segmented Interval Time Series Numerical Association Rule Mining**
## π¦ Installation
To install `NiaARMTS` with pip, use:
```sh
pip install niaarmts
```
## π Basic example
### Fixed Interval Time Series Numerical Association Rule Mining example
```python
from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS
# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('intervals.csv', timestamp_col='timestamp')
# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
dimension=dataset.calculate_problem_dimension(), # Adjust dimension dynamically
lower=0.0, # Lower bound of solution space
upper=1.0, # Upper bound of solution space
features=dataset.get_all_features_with_metadata(), # Pass feature metadata
transactions=dataset.get_all_transactions(), # Dataframe containing all transactions
interval='true', # Whether we're dealing with interval data
alpha=1.0, # Weight for support in fitness calculation
beta=1.0, # Weight for confidence in fitness calculation
gamma=1.0, # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
delta=1.0 # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)
# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100) # Run for 100 iterations
# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)
# Run the algorithm
best_solution = pso.run(task)
# Save discovered rules to CSV
niaarmts_problem.save_rules_to_csv("interval_rules.csv")
# Print all rules to the terminal
print("\n=== All Identified Rules (Interval Data, Sorted by Fitness) ===")
for idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):
print(f"\nRule #{idx}:")
print(f" Antecedent: {rule['antecedent']}")
print(f" Consequent: {rule['consequent']}")
print(f" Support: {rule['support']:.4f}")
print(f" Confidence: {rule['confidence']:.4f}")
print(f" Inclusion: {rule['inclusion']:.4f}")
print(f" Amplitude: {rule['amplitude']:.4f}")
print(f" Fitness: {rule['fitness']:.4f}")
print(f" Interval: {rule['start']} (start) to {rule['end']} (end)")
```
### Segmented Interval Time Series Numerical Association Rule Mining example
```python
from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS
# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('ts.csv', timestamp_col='timestamp')
# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
dimension=dataset.calculate_problem_dimension(), # Adjust dimension dynamically
lower=0.0, # Lower bound of solution space
upper=1.0, # Upper bound of solution space
features=dataset.get_all_features_with_metadata(), # Pass feature metadata
transactions=dataset.get_all_transactions(), # Dataframe containing all transactions
interval='false', # Whether we're dealing with interval data
alpha=1.0, # Weight for support in fitness calculation
beta=1.0, # Weight for confidence in fitness calculation
gamma=1.0, # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
delta=1.0 # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)
# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100) # Run for 100 iterations
# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)
# Run the algorithm
best_solution = pso.run(task)
# Output the best solution and its fitness value
print(f"Best solution: {best_solution[0]}")
print(f"Fitness value: {best_solution[1]}")
# Save all discovered rules to a CSV file
niaarmts_problem.save_rules_to_csv("discovered_rules.csv")
# Print all rules to the terminal
print("\n=== All Identified Rules (Sorted by Fitness) ===")
for idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):
print(f"\nRule #{idx}:")
print(f" Antecedent: {rule['antecedent']}")
print(f" Consequent: {rule['consequent']}")
print(f" Support: {rule['support']:.4f}")
print(f" Confidence: {rule['confidence']:.4f}")
print(f" Inclusion: {rule['inclusion']:.4f}")
print(f" Amplitude: {rule['amplitude']:.4f}")
print(f" Fitness: {rule['fitness']:.4f}")
print(f" Time window: {rule['start']} to {rule['end']}")
```
## π Reference Papers
Ideas are based on the following research papers:
[1] Iztok Fister Jr., DuΕ‘an Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.
[2] Iztok Fister Jr., Iztok Fister, Sancho Salcedo-Sanz. [Time Series Numerical Association Rule Mining for assisting Smart Agriculture](https://iztok.link/static/publications/298.pdf). In: International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022.
[3] I. Fister Jr., A. Iglesias, A. GΓ‘lvez, J. Del Ser, E. Osaba, I Fister. [Differential evolution for association rule mining using categorical and numerical attributes](http://www.iztok-jr-fister.eu/static/publications/231.pdf) In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.
[4] I. Fister Jr., V. Podgorelec, I. Fister. [Improved Nature-Inspired Algorithms for Numeric Association Rule Mining](https://iztok-jr-fister.eu/static/publications/324.pdf). In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.
[5] I. Fister Jr., I. Fister [A brief overview of swarm intelligence-based algorithms for numerical association rule mining](https://arxiv.org/abs/2010.15524). arXiv preprint arXiv:2010.15524 (2020).
[6] Fister, I. et al. (2020). [Visualization of Numerical Association Rules by Hill Slopes](http://www.iztok-jr-fister.eu/static/publications/280.pdf).
In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning β IDEAL 2020.
IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10
[7] I. Fister, S. Deb, I. Fister, [Population-based metaheuristics for Association Rule Text Mining](http://www.iztok-jr-fister.eu/static/publications/260.pdf),
In: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence,
New York, NY, USA, mar. 2020, pp. 19β23. doi: [10.1145/3396474.3396493](https://dl.acm.org/doi/10.1145/3396474.3396493).
[8] I. Fister, I. Fister Jr., D. Novak and D. Verber, [Data squashing as preprocessing in association rule mining](https://iztok-jr-fister.eu/static/publications/300.pdf), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 1720-1725, doi: [10.1109/SSCI51031.2022.10022240](https://doi.org/10.1109/SSCI51031.2022.10022240).
## See also
[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)
[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)
## π License
This package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.
## Disclaimer
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
## π Cite us
[1] Fister, I., Jr.; Salcedo-Sanz, S.; Alexandre-Cortizo, E.; Novak, D.; Fister, I.; Podgorelec, V.; Gorenjak, M. [Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture](https://doi.org/10.3390/math13132122). Mathematics 2025, 13, 2122. [https://doi.org/10.3390/math13132122](https://doi.org/10.3390/math13132122)
[2] Iztok Fister Jr., DuΕ‘an Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.
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"description": "<p align=\"center\">\n <img alt=\"logo\" width=\"300\" src=\".github/images/NiaARMTS.png\">\n</p>\n\n<h2 align=\"center\">\n Nature-Inspired Algorithms for Time Series Numerical Association Rule Mining\n</h2>\n\n<p align=\"center\">\n <img alt=\"PyPI version\" src=\"https://img.shields.io/pypi/v/niaarmts.svg\" />\n <img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/niaarmts.svg\">\n <a href=\"https://pepy.tech/project/niaarmts\">\n <img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/niaarmts.svg\">\n </a>\n <img alt=\"Downloads\" src=\"https://static.pepy.tech/badge/niaarmts\">\n <img alt=\"NiaARMTS\" src=\"https://github.com/firefly-cpp/NiaARMTS/actions/workflows/test.yml/badge.svg\" />\n <img alt=\"Documentation status\" src=\"https://readthedocs.org/projects/niaarmts/badge/?version=latest\" />\n</p>\n\n<p align=\"center\">\n <img alt=\"Repository size\" src=\"https://img.shields.io/github/repo-size/firefly-cpp/NiaARMTS\" />\n <img alt=\"License\" src=\"https://img.shields.io/github/license/firefly-cpp/NiaARMTS.svg\" />\n <img alt=\"GitHub commit activity\" src=\"https://img.shields.io/github/commit-activity/w/firefly-cpp/NiaARMTS.svg\">\n <a href=\"http://isitmaintained.com/project/firefly-cpp/NiaARMTS\">\n <img alt=\"Percentage of issues still open\" src=\"http://isitmaintained.com/badge/open/firefly-cpp/NiaARMTS.svg\">\n </a>\n <a href=\"http://isitmaintained.com/project/firefly-cpp/NiaARMTS\">\n <img alt=\"Average time to resolve an issue\" src=\"http://isitmaintained.com/badge/resolution/firefly-cpp/NiaARMTS.svg\">\n </a>\n <img alt=\"GitHub contributors\" src=\"https://img.shields.io/github/contributors/firefly-cpp/NiaARMTS.svg\"/>\n</p>\n\n<p align=\"center\">\n <a href=\"#-features\">\u2728 Features</a> \u2022\n <a href=\"#-installation\">\ud83d\udce6 Installation</a> \u2022\n <a href=\"#-basic-example\">\ud83d\ude80 Basic example</a> \u2022\n <a href=\"#-reference-papers\">\ud83d\udcda Reference Papers</a> \u2022\n <a href=\"#-license\">\ud83d\udd11 License</a> \u2022\n <a href=\"#-cite-us\">\ud83d\udcc4 Cite us</a>\n</p>\n\nThis framework is designed for **numerical association rule mining in time series data** using **stochastic population-based nature-inspired algorithms**[^1]. It provides tools to extract association rules from time series datasets while incorporating key metrics such as **support**, **confidence**, **inclusion**, and **amplitude**. Although independent from the NiaARM framework, this software can be viewed as an extension, with additional support for time series numerical association rule mining.\n\n[^1]: Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). [A brief review of nature-inspired algorithms for optimization](https://arxiv.org/abs/1307.4186). arXiv preprint arXiv:1307.4186.\n\n* **Free software:** MIT license\n* **Python**: 3.11, 3.12\n* **Documentation**: [https://niaarmts.readthedocs.io](https://niaarmts.readthedocs.io)\n\n## \u2728 Features\n\nThe current version of the framework supports two types of time series numerical association rule mining:\n\n- **Fixed Interval Time Series Numerical Association Rule Mining**\n- **Segmented Interval Time Series Numerical Association Rule Mining**\n\n## \ud83d\udce6 Installation\n\nTo install `NiaARMTS` with pip, use:\n\n```sh\npip install niaarmts\n```\n\n## \ud83d\ude80 Basic example\n\n### Fixed Interval Time Series Numerical Association Rule Mining example\n\n```python\nfrom niapy.algorithms.basic import ParticleSwarmAlgorithm\nfrom niapy.task import Task\nfrom niaarmts import Dataset\nfrom niaarmts.NiaARMTS import NiaARMTS\n\n# Load dataset\ndataset = Dataset()\ndataset.load_data_from_csv('intervals.csv', timestamp_col='timestamp')\n\n# Create an instance of NiaARMTS\nniaarmts_problem = NiaARMTS(\n dimension=dataset.calculate_problem_dimension(), # Adjust dimension dynamically\n lower=0.0, # Lower bound of solution space\n upper=1.0, # Upper bound of solution space\n features=dataset.get_all_features_with_metadata(), # Pass feature metadata\n transactions=dataset.get_all_transactions(), # Dataframe containing all transactions\n interval='true', # Whether we're dealing with interval data\n alpha=1.0, # Weight for support in fitness calculation\n beta=1.0, # Weight for confidence in fitness calculation\n gamma=1.0, # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted\n delta=1.0 # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted\n)\n\n# Define the optimization task\ntask = Task(problem=niaarmts_problem, max_iters=100) # Run for 100 iterations\n\n# Initialize the Particle Swarm Optimization algorithm\npso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)\n\n# Run the algorithm\nbest_solution = pso.run(task)\n\n# Save discovered rules to CSV\nniaarmts_problem.save_rules_to_csv(\"interval_rules.csv\")\n\n# Print all rules to the terminal\nprint(\"\\n=== All Identified Rules (Interval Data, Sorted by Fitness) ===\")\nfor idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):\n print(f\"\\nRule #{idx}:\")\n print(f\" Antecedent: {rule['antecedent']}\")\n print(f\" Consequent: {rule['consequent']}\")\n print(f\" Support: {rule['support']:.4f}\")\n print(f\" Confidence: {rule['confidence']:.4f}\")\n print(f\" Inclusion: {rule['inclusion']:.4f}\")\n print(f\" Amplitude: {rule['amplitude']:.4f}\")\n print(f\" Fitness: {rule['fitness']:.4f}\")\n print(f\" Interval: {rule['start']} (start) to {rule['end']} (end)\")\n```\n\n### Segmented Interval Time Series Numerical Association Rule Mining example\n\n```python\nfrom niapy.algorithms.basic import ParticleSwarmAlgorithm\nfrom niapy.task import Task\nfrom niaarmts import Dataset\nfrom niaarmts.NiaARMTS import NiaARMTS\n\n# Load dataset\ndataset = Dataset()\ndataset.load_data_from_csv('ts.csv', timestamp_col='timestamp')\n\n# Create an instance of NiaARMTS\nniaarmts_problem = NiaARMTS(\n dimension=dataset.calculate_problem_dimension(), # Adjust dimension dynamically\n lower=0.0, # Lower bound of solution space\n upper=1.0, # Upper bound of solution space\n features=dataset.get_all_features_with_metadata(), # Pass feature metadata\n transactions=dataset.get_all_transactions(), # Dataframe containing all transactions\n interval='false', # Whether we're dealing with interval data\n alpha=1.0, # Weight for support in fitness calculation\n beta=1.0, # Weight for confidence in fitness calculation\n gamma=1.0, # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted\n delta=1.0 # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted\n)\n\n# Define the optimization task\ntask = Task(problem=niaarmts_problem, max_iters=100) # Run for 100 iterations\n\n# Initialize the Particle Swarm Optimization algorithm\npso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)\n\n# Run the algorithm\nbest_solution = pso.run(task)\n\n# Output the best solution and its fitness value\nprint(f\"Best solution: {best_solution[0]}\")\nprint(f\"Fitness value: {best_solution[1]}\")\n\n# Save all discovered rules to a CSV file\nniaarmts_problem.save_rules_to_csv(\"discovered_rules.csv\")\n\n# Print all rules to the terminal\nprint(\"\\n=== All Identified Rules (Sorted by Fitness) ===\")\nfor idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):\n print(f\"\\nRule #{idx}:\")\n print(f\" Antecedent: {rule['antecedent']}\")\n print(f\" Consequent: {rule['consequent']}\")\n print(f\" Support: {rule['support']:.4f}\")\n print(f\" Confidence: {rule['confidence']:.4f}\")\n print(f\" Inclusion: {rule['inclusion']:.4f}\")\n print(f\" Amplitude: {rule['amplitude']:.4f}\")\n print(f\" Fitness: {rule['fitness']:.4f}\")\n print(f\" Time window: {rule['start']} to {rule['end']}\")\n```\n\n## \ud83d\udcda Reference Papers\n\nIdeas are based on the following research papers:\n\n[1] Iztok Fister Jr., Du\u0161an Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.\n\n[2] Iztok Fister Jr., Iztok Fister, Sancho Salcedo-Sanz. [Time Series Numerical Association Rule Mining for assisting Smart Agriculture](https://iztok.link/static/publications/298.pdf). In: International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022.\n\n[3] I. Fister Jr., A. Iglesias, A. G\u00e1lvez, J. Del Ser, E. Osaba, I Fister. [Differential evolution for association rule mining using categorical and numerical attributes](http://www.iztok-jr-fister.eu/static/publications/231.pdf) In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.\n\n[4] I. Fister Jr., V. Podgorelec, I. Fister. [Improved Nature-Inspired Algorithms for Numeric Association Rule Mining](https://iztok-jr-fister.eu/static/publications/324.pdf). In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.\n\n[5] I. Fister Jr., I. Fister [A brief overview of swarm intelligence-based algorithms for numerical association rule mining](https://arxiv.org/abs/2010.15524). arXiv preprint arXiv:2010.15524 (2020).\n\n[6] Fister, I. et al. (2020). [Visualization of Numerical Association Rules by Hill Slopes](http://www.iztok-jr-fister.eu/static/publications/280.pdf).\n In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2020.\n IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10\n\n[7] I. Fister, S. Deb, I. Fister, [Population-based metaheuristics for Association Rule Text Mining](http://www.iztok-jr-fister.eu/static/publications/260.pdf),\n In: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence,\n New York, NY, USA, mar. 2020, pp. 19\u201323. doi: [10.1145/3396474.3396493](https://dl.acm.org/doi/10.1145/3396474.3396493).\n\n[8] I. Fister, I. Fister Jr., D. Novak and D. Verber, [Data squashing as preprocessing in association rule mining](https://iztok-jr-fister.eu/static/publications/300.pdf), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 1720-1725, doi: [10.1109/SSCI51031.2022.10022240](https://doi.org/10.1109/SSCI51031.2022.10022240).\n\n## See also\n\n[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)\n\n[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)\n\n## \ud83d\udd11 License\n\nThis package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.\n\n## Disclaimer\n\nThis framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!\n\n## \ud83d\udcc4 Cite us\n\n[1] Fister, I., Jr.; Salcedo-Sanz, S.; Alexandre-Cortizo, E.; Novak, D.; Fister, I.; Podgorelec, V.; Gorenjak, M. [Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture](https://doi.org/10.3390/math13132122). Mathematics 2025, 13, 2122. [https://doi.org/10.3390/math13132122](https://doi.org/10.3390/math13132122) \n\n[2] Iztok Fister Jr., Du\u0161an Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.\n",
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