Adatsorok

Adatsorok, or data series, refer to sequences of data points collected or observed over time or across categories. They are a fundamental object in statistics, econometrics, and data analysis. An adatsor can be univariate (one variable) or multivariate, and it may be recorded at regular intervals (time series) or at irregular occasions. When observations are indexed by time, the term time series is commonly used, with components such as trend, seasonality, and irregular fluctuations.

Time series analysis seeks to understand patterns in adatsorok and to forecast future values. Key concepts include autocorrelation, stationarity, and decomposition into trend, seasonal, and irregular components. Models such as ARIMA, exponential smoothing, and state-space methods describe the data and generate predictions. Panel data combines multiple adatsorok that share cross-sectional units across time, enabling the analysis of both temporal and cross-sectional variation.

Storage and formats vary by ecosystem. Adatsorok are typically stored as one-dimensional arrays or vectors with an associated index. In R they can be represented as ts, zoo, or xts objects; in Python's pandas as Series with a datetime- or PeriodIndex; in Matlab as timeseries objects. Data handling may involve resampling, missing data imputation, smoothing, and transformation to improve modeling assumptions.

Applications of adatsorok span finance (asset prices and risk), meteorology (temperature, precipitation), economics (income and output indicators), engineering (sensor measurements), and epidemiology (case counts). Data quality issues such as missing values, irregular sampling, and measurement error require preprocessing and careful modeling to obtain reliable insights.