Estimate Trend at a Point in a Noisy Time Series (github.com)

A new open-source package called "incline" has been introduced to help researchers and practitioners estimate trends in noisy time series data more effectively. By utilizing local higher order polynomials via Savitzky-Golay and smoothing splines, the package allows users to smooth time series and compute both first and second derivatives at specified points. This capability is crucial for uncovering hidden trends in dynamic environments, such as sudden changes in product prices or patient health indicators due to market fluctuations or health crises. The significance of incline lies in its ability to provide more accurate trend estimates compared to naive methods, which only consider immediate neighboring values. The difference between naive estimates and those obtained through smoothing techniques can be substantial, with one example showing a correlation of -0.47. The package standardizes the interface for local smoothing and derivative estimation across thousands of time series, making it a valuable tool for data analysis in sectors reliant on time series data, including finance and healthcare. With user-friendly functions that accommodate various inputs and output detailed data frames, incline promises to enhance analytical capabilities in the AI and machine learning community.