Time Series Analysis

Time Series Analysis

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CD Genomics' bioinformatics experts are able to provide time series analysis services for a wide range of histological data to meet our customers' individual needs.

Introduction of Time Series Analysis

Time series analysis is a chronological, time-varying, and interrelated data series. The collection of sequences of discrete numbers obtained at a series of moments from observational measurements of a variable or a group of variables is called a time series. The four types of variation in a time series are seasonal variation, trend variation, cyclical variation, and random variation.

The power of LSAres and DDLSA in testing for the local association of two time series data under the bivariate AR model.Figure 1. The power of LSAres and DDLSA in testing for the local association of two time series data under the bivariate AR model. (Zhang F, et al., 2019)


Time series data analysis is performed by collecting data from different time points, and can be used for but is not limited to the following research:

  • Time series analysis can be used to analyze the stability of microbial communities and their response to disturbances.
  • Time series analysis is used to study the gene expression pattern of a sample at multiple time points within a time period.
  • Electronic health record data analysis.
  • Bayesian optimization.
  • Time series causal analysis.
  • Protein sequence analysis.
  • Irregularly sampled time series modeling.
  • Time series prediction.

CD Genomics Time Series Analysis Pipeline

CD Genomics Time Series Analysis Pipeline

Time Series Analysis Content

Pre-processing of time series Stability test
Purely randomness test
Smooth time series analysis ARMA model
Qualitative analysis of non-stationary series Trend fitting method
Smooth method
Randomness analysis of non-stationary series ARIMA model
Sparse coefficient model
Seasonal model
Residual autoregressive model
Conditional heteroskedasticity model
Multivariate time series analysis False return
Unit root test
Co-integration test
Error correction model

We are able to use time series analysis to help our customers achieve high throughput data analysis in multi-omics. For questions about analysis content, project cycle, and pricing, please click online inquiry.

How It Works

CD Genomics is a professional bioinformatics service provider with years of experience in NGS and long-read sequencing (PacBio SMRT and Oxford Nanopore platforms) data, proteomics and metabolomics data analysis, integrated analysis services, database construction, and other bioinformatics solutions.

How It Works

CD Genomics has professional bioinformatics experts who have successfully provided time series analysis to researchers in many different fields. Our professional skills and enthusiasm will provide you with high-quality analysis services. If you are interested in our services, please contact us for more details.


  1. Macnair W, et al. psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data. Bioinformatics. 2022 Jun 24; 38(Suppl 1): i290-i298.
  2. Zhang F, et al. Statistical significance approximation for local similarity analysis of dependent time series data. BMC Bioinformatics. 2019 Jan 28; 20(1): 53.
* For Research Use Only. Not for use in diagnostic procedures.
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