Forecasting is made because of the complexity and uncertainty faced by decision makers (5 = 0.9). The use these two forecasting methods to compare which method more accurate closer true value. research used starts from collecting data, determining forecast methods, calculating forecasts, selecting drawing conclusions. calculation estimated results for F&B costs at Puri Santrian Hotel Sanur ...

A recursive formulation of an exponential smoothing filter is developed, within the framework of a least square error approach with data uncertainties that increase exponentially with time. An efficient implementation into Java is presented. By analogy to the Kalman filter, an interpretation of the gain as a ratio of uncertainties leads to a measure of validity for the recursive exponential fil...

This thesis investigates computational methods for pricing complex path-dependent derivative securities, especially geometricand arithmetic-moving-average-lookback options. The latter security was rst issued by Polaris Securities in 1999. Our methodology can be easily modi ed to price similarly structured options issued by other securities rms. The moving-average-lookback option is a call optio...

The training of precise speech recognition models depends on accurate segmentation of the phonemes in a training corpus. Segmentation is typically performed using HMMs, but recent speech recognition work suggests that the transient acoustic features characteristic of manner-class phoneme boundaries (landmarks) may be more precisely localized using acoustic classifiers specifically designed for ...

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with exponential moving average (EMA) technique to reduce noise in and improve accuracy prediction. compares performance EMA other commonly used learning models, including LSTM, GR...

A popular smoothing technique commonly used in time series analysis is double exponential smoothing. Basically, it’s an improvement of simple exponential smoothing which does the exponential filter process twice. Many researchers had developed the technique, hence Brown’s double exponential smoothing and Holt’s double exponential smoothing. Here, we introduce a new approach of double exponentia...

For the stationary invertible moving average process of order one with unknown innovation distribution F , we construct root-n consistent plug-in estimators of conditional expectations E(h(Xn+1)|X1, . . . , Xn). More specifically, we give weak conditions under which such estimators admit Bahadur type representations, assuming some smoothness of h or of F . For fixed h it suffices that h is loca...