Support Vector Regression
Support Vector Regression (SVR) is a supervised learning technique in SVMs that aims to find the hyperplane in a high-dimensional feature space that best fits the training data and minimizes the prediction error for regression tasks. SVR is a technique used to predict continuous values. In time series forecasting with SVR, it’s considered a regression task.
SVR works by drawing a line (in simpler cases) or a surface (in more complex situations) that best fits the data points. Regression aims to predict a continuous target variable based on one or more input features.
- In time series forecasting, the target variable is the future value of a time series (e.g., stock price at a future date, temperature at a future time step). SVR, as a regression technique, learns a model that maps historical time series data (features) to the corresponding future values (target variable).
- The output of SVR in time series forecasting is a continuous value representing the predicted future value of the time series.
Time Series Forecasting with Support Vector Regression
Time series forecasting is a critical aspect of data analysis, with applications spanning from financial markets to weather predictions. In recent years, Support Vector Regression (SVR) has emerged as a powerful tool for time series forecasting due to its ability to handle nonlinear relationships and high-dimensional data. In this project, we’ll delve into time series forecasting using SVR, focusing specifically on forecasting electric production of next 10 months.
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