There are several mathematical methods to find anomalies in time series data, including:

1. Statistical Methods: This method includes techniques such as Z-score, Grubbs' Test, and Boxplot methods.

2. Machine Learning Methods: Techniques such as Clustering, Classification, and Regression can be used for detecting anomalies.

3. Fourier Transform: It is used to identify anomalies in time series data by breaking the series into cyclic components.

4. Wavelet Transform: It provides a multi-scale decomposition of the time series data and enables the detection of anomalies across different scales.

5. ARIMA modeling: It includes the use of Autoregressive Integrated Moving Average models to forecast the future values and detect unusual behavior in the data.

Some of the relevant scientific publications on this topic are:

1. "Anomaly Detection Techniques for Time Series Data" by S. Chandola, A. Banerjee, and V. Kumar.

2. "A Survey of Anomaly Detection Techniques in Text Stream Data" by M. Mohiuddin, H. Yu, and O. Rana.

3. "Robust Anomaly Detection for Time Series Data" by D. Dua and P. Kumar.

4. "A Machine Learning Approach for Anomaly Detection in Time Series Data" by A. Ahmed, A. Mahmood, and M. Waseem.

5. "Wavelet-Based Time Series Anomaly Detection and Classification" by C. Chen, Y. Li, and X. Liang.
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