aegridaanalüüs

Aegridaanalüüs, meaning "time series analysis" in Estonian, is a statistical method used to analyze time-ordered data points. These data points are collected, recorded, or observed over a period of time at regular intervals. The primary goal of time series analysis is to understand the underlying structure of the data, identify patterns such as trends, seasonality, and cycles, and use this understanding to forecast future values.

The fundamental assumption in time series analysis is that the order of observations matters. Unlike cross-sectional data, where observations are independent, time series data exhibits dependence between consecutive observations. This dependence can be captured through various statistical models. Common models include autoregressive (AR) models, which assume that the current value depends on past values, moving average (MA) models, where the current value depends on past forecast errors, and combinations of these, such as ARMA and ARIMA (autoregressive integrated moving average) models. Seasonal variations are often handled by extensions like SARIMA (seasonal ARIMA).

Applications of time series analysis are widespread across various disciplines. In economics, it's used to forecast stock prices, inflation rates, and GDP. In meteorology, it helps predict weather patterns and climate change. In finance, it's crucial for risk management and algorithmic trading. Other fields include engineering, biology, and social sciences, where understanding and predicting phenomena that evolve over time is essential. The process typically involves data exploration, model identification, parameter estimation, diagnostic checking, and forecasting.