scientific-time-series

You can forecast time-series data by applying ARIMA, SARIMA, or Prophet modeling techniques to generate short- and long-range predictions, utilizing the built-in templates to quickly fit your dataset and produce forecast plots.

STL decomposition separates time-series data into trend, seasonal, and residual components. You need it to isolate underlying patterns from noise, allowing for more accurate modeling and anomaly detection in measurement data.

You can detect change-points and anomalies using PELT and Bayesian approaches provided as templates. These methods identify statistical shifts and outliers in process data, environmental monitoring, or clinical telemetry signals.

Yes, frequency-domain analysis using FFT and wavelets identifies hidden periodic cycles in time-series data. This helps uncover repeating patterns across lab and field measurements for robust insights.

Yes, Granger causality testing explores relationships across multiple time series. It determines whether one series can predict another, enabling Granger-based relationship exploration within your dataset.

The best way to start is to provide your time-series dataset to run STL decomposition and generate an initial forecast. This establishes a baseline trend and seasonal analysis for further modeling.