tímaröðir

Tímaröðir, meaning time series in Icelandic, refers to a sequence of data points collected or recorded over time. These data points are typically ordered chronologically, with each observation occurring at a specific point in time or within a defined time interval. Time series data is prevalent across various fields, including economics, finance, meteorology, engineering, and social sciences, providing insights into trends, patterns, and behaviors that evolve over time.

The fundamental characteristic of time series data is its temporal dependency. Observations in a time series are often not independent; the value at one point in time can be influenced by previous values. This dependency allows for analysis aimed at understanding past behavior and forecasting future values. Key components often analyzed in time series include trend, seasonality, cyclical patterns, and irregular fluctuations.

Analyzing time series data involves various statistical and machine learning techniques. These methods aim to model the underlying structure of the data, identify significant patterns, and make predictions. Common techniques include moving averages, exponential smoothing, ARIMA (AutoRegressive Integrated Moving Average) models, and more advanced deep learning approaches like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. The goal is often to forecast future values, detect anomalies, or understand the driving forces behind the observed temporal changes.