Aikasarjat

Aikasarjat (time series) are sequences of data points recorded at successive points in time, typically at regular intervals. They are used to analyze how a variable evolves over time and to forecast future values based on historical patterns. The observations are ordered, and the spacing between measurements matters for the analysis.

Common components of aikasarjat include trend (long-term progression), seasonality (regular patterns within a year or shorter cycles), cyclicality (irregular, longer cycles), and irregular variation (noise). Many analyses assume stationarity, meaning the statistical properties do not change over time; non-stationary series are often differenced or transformed to stabilize mean and variance.

Techniques for analyzing aikasarjat include decomposition into components, smoothing methods such as moving averages and exponential smoothing, and forecasting models such as ARIMA and SARIMA, state-space models, and modern approaches like Prophet. Model selection relies on diagnostics and information criteria such as AIC or BIC, while forecast accuracy is evaluated with out-of-sample tests or cross-validation.

Applications of aikasarjat span economics, finance, energy demand, meteorology, and epidemiology. Data issues include missing values, outliers, structural breaks, and changing measurement practices, all of which can affect model choice and forecast reliability.

Limitations involve nonlinear dynamics, high-frequency or multivariate time series, and non-stationarity that is difficult to model. Proper handling requires careful preprocessing, diagnostic checks, and an awareness of the underlying domain context.