Unlocking the Secrets of Time Series Data: A Step-by-Step Guide
Time series profiles are all around us, influencing our daily lives and driving specialized research across various fields. But have you ever wondered how to derive meaningful insights from a noisy time series profile, especially when data is scarce?
In its simplest form, a time series profile consists of a sequence of data points y(0), y(1), … ,y(t), with each point at time t dependent on the preceding point at time t-1 or even further back.
When it comes to predicting the future behavior of such profiles, various modeling approaches exist. These models typically utilize information from the past or present to estimate how the profile will evolve over time. From neural networks for weather forecasting to deep learning for stock price predictions, the possibilities are endless. Research works like those of Bi et al. (2023), Xiao and Su (2022), and Rathipriya et al. (2023) showcase the vast applications of time series predictions across different domains.