Add to ORKGInternational audienceMany time series forecasting problems require the estimation of possibly inaccurate, but long¬term, trends, rather than accurate short-term prediction. In this paper, a double use of the Self-Organizing Map algorithm makes it possible to build a model for long¬term prediction, which is proven to be stable. The method uses the information on the structure of the series when available, by predicting blocs instead of scalar values. It is illustrated on real time series for both scalar and bloc predictions
Electricity load forecasting provides the critical information required for power institutions and a...
Electricity load prediction is an essential tool for power system planning, operation and manage-men...
Time series prediction plays a pivotal role in various areas, including for example finance, weather...
International audienceMany time series forecasting problems require the estimation of possibly inacc...
Many time series forecasting problems require the estimation of possibly inaccurate, but longterm, t...
à la suite de la conférence ANNPR, Florence 2003International audienceKohonen self-organisation maps...
A la suite de la conférence WSOM 03 à KitakiushuInternational audienceThe Kohonen self-organization ...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
The Kohonen self-organization map is usually considered as a classification or clustering tool, with...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
A newly proposed Recurrent Self-Organizing Map (RSOM) is studied in time series prediction. In this ...
International audienceThe Double Vector Quantization method, a long-term forecasting method based on...
International audienceWe present two approaches for linear prediction of long-memory time series. Th...
A two-stage forecasting approach for long memory time series is introduced. In the first step, we es...
Electricity load forecasting provides the critical information required for power institutions and a...
Electricity load prediction is an essential tool for power system planning, operation and manage-men...
Time series prediction plays a pivotal role in various areas, including for example finance, weather...
International audienceMany time series forecasting problems require the estimation of possibly inacc...
Many time series forecasting problems require the estimation of possibly inaccurate, but longterm, t...
à la suite de la conférence ANNPR, Florence 2003International audienceKohonen self-organisation maps...
A la suite de la conférence WSOM 03 à KitakiushuInternational audienceThe Kohonen self-organization ...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
The Kohonen self-organization map is usually considered as a classification or clustering tool, with...
Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety ...
A newly proposed Recurrent Self-Organizing Map (RSOM) is studied in time series prediction. In this ...
International audienceThe Double Vector Quantization method, a long-term forecasting method based on...
International audienceWe present two approaches for linear prediction of long-memory time series. Th...
A two-stage forecasting approach for long memory time series is introduced. In the first step, we es...
Electricity load forecasting provides the critical information required for power institutions and a...
Electricity load prediction is an essential tool for power system planning, operation and manage-men...
Time series prediction plays a pivotal role in various areas, including for example finance, weather...