Time series forecasting involves analyzing sequential data measured over time. A time series can be univariate (containing a single variable) or multivariate (containing multiple variables). It can also be continuous or discrete. Key components of time series include trends, cyclical variations, seasonal variations, and irregular variations. Time series analysis involves fitting a model to the data. Stationarity, where the statistical properties do not depend on time, is required for forecasting. Common forecasting models include ARMA, ARIMA, and SARIMA stochastic models as well as artificial neural networks and support vector machines. Each approach has strengths for modeling nonlinear relationships and generalizing to make predictions.
4. Time Series
Univariate
A time series
containing records
of a single variable.
Multivariate
A time series
containing records of
more than one variable.
36. Time series forecasting using ANNs
◉ Universal functional approximators.
◉ Solve Nonlinear problems.
◉ Data Driven.
◉ Generalization.
◉ Tolerate noise in data.
39. Time series forecasting using SVM
◉ Function Approximation.
◉ Good Generalization.
◉ Optimal Separating Hyperplane.
◉ Regression Problems.
◉ Time Series Predication.