Seasonal clustering technique for time series data

Inverse forecasting: A new approach for predictive modeling

“…The following theorem shows an important advantage of applying the inverse linear programming technique as an alternative method for forecasting comparing with the other methods in the literature (see for example, Inniss, 2006;Mohammadi et al, 2006;Bermú dez et al, 2006;Taylor, 2007). The computation efficiency of the new model is that there is no need to solve any linear programming model since the optimal solution can be obtained as a compact form.…”

“…On the other hand time-series modeling and forecasting continues to be an important area in both academic research and practical application. For example, Inniss (2006) developed a seasonal clustering technique for determining clusters of time series data. His model was also applied to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for seasonal forecasting and planning (Inniss, 2006).…”

“…For example, Inniss (2006) developed a seasonal clustering technique for determining clusters of time series data. His model was also applied to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for seasonal forecasting and planning (Inniss, 2006). As the practical application of time-series modeling Bermú dez, Segura, and Vercher (2006) suggested a nonlinear multi-objective problem for forecasting of time series based on soft computing.…”

Towards applying text mining and natural language processing for biomedical ontology acquisition

“…To determine if resulting sets of terms from text mining are the most effective for describing a disease, several similarity measures such as cosine similarity can be used. In [20], Inniss developed a clustering technique in which she evaluated several measures of (dis)similarity using an effectiveness measure she developed. It should be noted that results of SAS text miner will differ based on the weighting schemes that are used.…”

A novel SON2‐based similarity index and its application for the rationalization of river water quality monitoring network

“…Artificial neural networks are a very useful technique for the classification, regression, and clustering of large amounts of complex data (Hsu & Li, ). Among several artificial neural network architectures, the self‐organizing map (SOM), which performs unsupervised clustering, is often used for ascertaining trends and patterns in data (Inniss, ). For data visualization and clustering, an SOM with two‐dimensional output grid (Kohonen layer) has frequently been applied, whereas a one‐dimension SOM or SON (self‐organizing network) has mainly been used for the classification of patterns (Crnković et al, ; Deljanin et al, ).…”