# Online Forecasting and Anomaly Detection Based on the ARIMA Model
## 2 Materials and Methods
### Anomaly Detection Algorithms
#### 2.4.2 One Point Prediction
The ARIMAFD Python3 library described in the Materials and methods section can be installed at [[https://pypi.org/project/arimafd/](https://pypi.org/project/arimafd/)]([https://pypi.org/project/arimafd/](https://pypi.org/project/arimafd/)) (accessed on 1 April 2021). Experiment pipeline and other materials are available at [[https://github.com/waico/experiment_arimafd](https://github.com/waico/experiment_arimafd)]([https://github.com/waico/experiment_arimafd](https://github.com/waico/experiment_arimafd)) (accessed on 1 April 2021). Moreover, additional results obtained using the developed algorithm and not included in this paper can be found at [[https://github.com/waico/SKAB](https://github.com/waico/SKAB)]([https://github.com/waico/SKAB](https://github.com/waico/SKAB)) (accessed on 1 April 2021). The authors declare no conflicts of interest. ## References

* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Real-time anomaly detection for streaming analytics. arXiv2016, arXiv:1607.02480. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] A survey of methods for time series change point detection. Knowl. Inf. Syst.2017, _51_, 339-367. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2008) [PERSON]; [PERSON] Modelling unemployment rate using Box-Jenkins procedure. J. Appl. Quant. Methods2008, \(3\), 156-166. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON] Comparative study of ANFIS and ARIMA model for weather forecasting in Dhaka. In Proceedings of the 2013 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 17-18 May 2013. [CrossRef]
* [PERSON] (2015) [PERSON]; [PERSON] _Applied Econometrics_; Macmillan International Higher Education: London, UK, 2015. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Time Series Analysis: Forecasting and Control_; John Wiley & Sons: Hoboken, NJ, USA, 2015. * [PERSON] et al. (2002) [PERSON]; [PERSON]; [PERSON] _Introduction to Time Series and Forecasting_; Springer: New York, NY, USA, 2002; Volume 2. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] _Time Series Analysis and Its Applications: With R Examples_; Springer: New York, NY, USA, 2017. * [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark. In Proceedings of the 2015 IEEE 14 th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 9-11 December 2015. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Unsupervised Anomaly Event Detection for Cloud Monitoring Using Online Arima. In Proceedings of the 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), Zurich, Switzerland, 17-20 December 2018. [CrossRef]
* [PERSON] (2003) [PERSON] Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20 th International Conference on Machine Learning (ICML-03), Washington, DC, USA, 21-24 August 2003; pp. 928-936. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON] Online Learning for Time Series Prediction. _arXiv_**2013**, arXiv:1302.6927v1. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibility and potentials. _Harmful Algae_**2015**, 43, 58-65. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON]; [PERSON] Renewable energy management in cellular networks: An online strategy based on ARIMA forecasting and a Markov chain model. In Proceedings of the 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, 3-6 April 2016. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Online Truth Discovery on Time Series Data. In _Proceedings of the 2018 SIAM International Conference on Data Mining_; Society for Industrial and Applied Mathematics: Philadelphia, PA, USA, 2018; pp. 162-170. [CrossRef]
* [PERSON] et al.