The First Complaint: An Approach to the Admission of Child-Hearsay Statements Under the Alaska Rules of Evidence

The Swedish Transport Agency has for a long time collected data on a monthly basis for different variables that are used to make predictions, short projections as well as longer projections. They have used SAS for producing statistical models in air transport. The model with the largest value of coefficient of determination is the method that has been used for a long time. The Swedish Transport Agency felt it was time for an evaluation of their models and methods of how projections is estimated, they would also explore the possibilities to use different, completely new models for forecasting air travel. This Bachelor thesis examines how the Holt-Winters method does compare with SARIMA, error terms such as RMSE, MAPE, R2, AIC and BIC  will be compared between the methods.  The results which have been produced showing that there may be a risk that the Holt-Winters models adepts a bit too well in a few variables in which Holt-Winters method has been adapted. But overall the Holt-Winters method generates better forecasts .Avbryt / Spara utkast</p

Load forecasting using holt-winters method

The global demand for energy is increasing daily due to the constant growing of the civilization with the expansion of energy infrastructure and the addition of new needs. This load increments added to fossil fuels crisis and the global economic crisis, arises the need to minimize both the electricity consumption and the economic expenditure [1][2]. The National Grids operates the transmission network that connects power stations to electricity consumers. Is also responsible for balancing the demand and supply system and ensuring that power stations will be able to provide electricity in case of an unanticipated increase in electricity [3]. As energy cannot be stored it must be generated according to demand, in order to avoid both the wastage of energy sources as the economic one. [4] So, this is what this Thesis is about, study different algorithms capable of predicting load consumption

FORECASTING THE NUMBER OF FOREIGN TOURISM IN BALI USING THE HYBRID HOLT-WINTERS-ARTIFICIAL NEURAL NETWORK METHOD

Bali was one of the destinations frequently visited by tourists because it had natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 had increased, but there had been a very significant decrease in 2020. Forecasting the number of tourists coming to Bali in the future was needed to provide input or recommendations to the government and business people in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used was the Holt-Winters method. The Holt-Winters method was part of Exponential Smoothing which is based on smoothing stationary, trend and seasonal elements. However, the Holt-Winters method can only capture linear patterns, so a method was needed that can capture non-linear patterns. The Artificial Neural Network method was proposed to overcome the shortcomings of the Holt-Winters Method. This research was focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists was the Hybrid Holt-Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by the MAD value of 0.036684, MSE 0.01098698 and MAPE 6.30417%

New Estimation Rules for Unknown Parameters on Holt-Winters Multiplicative Method

The Holt-Winters method is a well-known forecasting method used in time-series analysis to forecast future data when a trend and seasonal pattern is detected. There are two variations, i.e. the additive and the multiplicative method. Prior study by Vercher, et al. in [1] has shown that choosing the initial conditions is very important in exponential smoothing models, including the Holt-Winters method. Accurate estimates of initial conditions can result in better forecasting results. In this research, we propose new estimation rules for initial conditions for the Holt-Winters multiplicative method. The estimation rules were derived from the original initial conditions combined with the weighted moving average method. From the experimental results it was found that the new approach of the Holt-Winters multiplicative method can outperform the original Holt-Winters multiplicative method

Anomaly Detection and Prediction Based on Holt-Winters Method

Outliers, detection and prediction of abnormal periods, and prediction of trends in a given time base on existing data are the first problems to be solved in intelligent operation and maintenance. This paper took KPI performance index of 58 cells covered by 5 base stations from August 28 to September 25, 2021 as research data, chose 3 indexes (average number of users, PDCP traffic and average activation number), established a set of outlier prediction system based on Holt-Winters method

Forecasting demand: development of a fuzzy growth adjusted holt-winters approach

Irrespective of the type of items manufactured by an industry, environment is now becoming progressively more and more competitive than the past few decades. To sustain in this severe competition, companies have no choice but to manage their operations optimally and in this respect the importance of more accurate demand prediction cannot be exaggerated. This research presents a forecasting approach tailoring the multiplicative Holt-Winters method with growth adjustment through incorporation of fuzzy logic. The growth parameter of the time series values is adjusted with the conventional Holt-Winters method and tested for predicting the real-life demand of transformer tank experienced by a local company. The result obtained by applying the new approach shows a significant improvement in the accuracy of the forecasted demand and sheds light on further enhancement of the proposed method by optimizing other time series parameters through fuzzy logic application for possible application in prediction of demand having trend, seasonal and cyclical changes

Using grey Holt-Winters model to predict the air quality index for cties in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe randomness, non-stationarity and irregularity of air quality index series bring the di fficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal e ects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy

Analisa Tingkat Inflasi di Indonesia Menggunakan Metode Holt-Winters

This research aims to model the change in inflation rate in Indonesia. Inflation is defined as an increase in the price of goods or services that occur continuously. Ifthe known changes in a period of inflation it will be known the value of inflation in that period. Then from the model obtained, is expected to predict the value of inflation in Indonesia in the next period. By utilizing the data time series methods in use in this research is a method of Holt-Winters. Method of The Holt-Winters' (HW) smoothing is a generalization of the method of linear Holt. This technique was proposed in 1960 by Holt and Winters. This method is the development of a linear method of Holt. Holt-Winters method widely used in time series which shows a pattern of increase or decrease in the trend. By utilizing these methods shows that data prediction and the actual data is almost the same. From these data, there is a lower prediction datafrom actual data. Forecasting with Holt-Winters method can be said to be efficient ifat the time of the next level of inflation other than in the outermost color range class prediction

Holt-Winters method with missing observations and its actuarial application

Název práce: Holtova-Wintersova metoda s chybějícími pozorováními a její aktuárské aplikace Autor práce: Jiří Gregor Katedra: Katedra pravděpodobnosti a matematické statistiky Vedoucí bakalářské práce: prof. RNDr. Tomáš Cipra, DrSc. Katedra vedoucího práce: Katedra pravděpodobnosti a matematické statistiky Abstrakt: Práce stručně popisuje problematiku časových řad a sezónnosti. Představuje různé přístupy k vyrovnání a predikci časových řad. Hlavní část práce je věnována Holtově - Wintersově metodě pro časové řady s chybějícími pozorováními a její následná aplikace na aktuárská data. Klíčová slova: Časové řada, vyrovnávání, Holtova - Wintersova metoda s chybějícími pozorováními 1Title: Holt-Winters method with missing observations and its actuarial application Author: Jiří Gregor Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Tomáš Cipra, DrSc. Supervisor's department: Department of Probability and Mathematical Statistics Abstract: This thesis describes problematics of time series and its seasonal component. It shows different approaches to smoothing and prediction of time series. The main part of thesis is devoted to Holt-Winters method with missing observations and its actuarial application. Keywords: Time serie, smoothing, Holt-Winters method with missing observations 1Department of Probability and Mathematical StatisticsKatedra pravděpodobnosti a matematické statistikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic