Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 12 of 12 for “"Holt Winters"”.
-
Forecasting of work in process quality using Holt-Winters method for missing observations
… develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using …
-
Optimizing smoothing parameters for the triple exponential forecasting model
… method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated …
-
Structure combination of forecasting models with application in the energy sector
… in separate applications, multiplicative Holt-Winters and multiplicative Holt-Winters-Taylor models. Noise addition and block swapping were applied to the original time series in order to generate structurally diverse individual models. Applications were conducted using a seasonal daily …
-
Modeling and prediction of wind power data
… including ARIMA model, SARIMA model, ARAR model, Holt-Winters method, and a state-space model. We compared the difference between the predicted data and the original data. We conclude that a state space model incorporating trend and seasonal variables, a Kalman prediction filter, with the …
-
Modeling with Attention in Demand Forecasting and Beyond
… with more traditional time series models such as Holt-Winters as a way to alleviate some of the predictive responsibility from the Transformer which require relatively large amounts of data to train as compared to traditional time series methods.
-
Cyber security risk analysis framework : network traffic anomaly detection
… ETS, Seasonal ARIMA, TBATS, Double-Seasonal Holt-Winters, and Ensemble methods) and Long Short-Term Memory Recurrent Neural Network algorithm. Upon creating the baselines and forecasting network traffic trends, the anomaly detection algorithm was implemented using specific thresholds to …
-
Forecasting demand for district heating using different forecasting methods
… model was an exponential smoothing model, the Holt-Winters method, a model that offers to take into account trend and seasonality. Finally, two machine learning models were created, LSTM and Random Forest. The LSTM model had only access to usage when trained, like the ARIMA model, but the …
-
Error magnitude and directional accuracy for time series forecasting evaluation
… between SARIMA, time series regression, Holt-Winter, intervention neural network and fuzzy time series. The root mean square error, mean absolute percentage error, mean absolute deviation, Fisher’s exact test, Chi-square test, directional accuracy, directional value and the modified of …
-
Admission Control in Sliced Networks, with Predictive Analytics
… forecasting model was implemented based on the Holt-Winters Exponential Smoothing predictive model. This forecasting model was trained using the network data from the real IP network dataset and then incorporated into the admission control process. For prediction-based admission control, the …
-
Forecasting international movements of Returnable Transport Items
… space seasonal exponential, SARIMA, state space Holt-Winters, and multivariate regression methods. These four methods were then used to predict the pallet flows using two different approaches. In the first approach, two separate forecasting models were developed, one for the United …
-
Detecting Irregular Energy Consumption Through Analytical Techniques
… moving average (DSARlMA) and the double seasonal Holt-Winters exponential<br/>smoothing, which have been used as benchmarks. The prediction limits of the benchmarks and the modified time series method were evaluated to examine if they are able to detect irregular electricity demand which have been …
-
Value chain diversification in the sugar industry using quantitative economic forecasting models
… moving averages, simple exponential smoothing, Holt's method, Holt-Winters' method and Auto-Regressive Integrated Moving Average (ARIMA) models. Each type of model was analysed in the context of the eight industries' data, from which ARIMA models were identified as those which were broad enough …