A collection of data points gathered or recorded over time, with each data point linked to a unique timestamp, is called a time series. Since the data points are usually arranged chronologically, trends, patterns, and behaviors throughout time can be examined. Many disciplines, including signal processing, finance, economics, and environmental research, frequently employ time series data.
Important features of time series data consist of:
Temporal Order: A measurement or observation at a specific time is represented by each data point, which is sorted in a particular order based on time.
Trends: Long-term movements or patterns that can point to underlying changes or developments are commonly seen in time series data.
Seasonality: Some time series data displays repeating patterns or cycles, known as seasonality, which may be influenced by regular, periodic factors like seasons, months, or days of the week.
Irregularity and Noise: Time series data can also contain irregularities and random fluctuations, referred to as noise, making it important to distinguish between true patterns and random variations.
Analyzing time series data involves various techniques, such as statistical methods, machine learning models, and forecasting approaches, to uncover insights, make predictions, or understand underlying dynamics.