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Analyze and economic time series forecasting by using selected statistical methods
Skopal, Martin ; Charvát, Pavel (referee) ; Mauder, Tomáš (advisor)
V této diplomové práci se zaměřujeme na vytvoření plně automatizovaného algoritmu pro předpovědi finančních řad, který se snaží využít kombinační proceduru na dvou úrovních mezi dvěma rodinami předpovědních modelů, Box-Jenkins a Exponenciální stavové modely, které jsou schopny modelovat jak homoskedastické tak heteroskedastické časové řady. Pro tento účel jsme navrhli selekční proceduru v prostředí MATLAB pro modely ARIMA. Výsledný kombinovaný model je pak aplikován několik finančních časových řad a jeho výkonost je diskutována.
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The Forecasting Model of Demand in the Textile Industry
Kunc, Tomáš ; Oulehla, Jiří (referee) ; Luňáček, Jiří (advisor)
Thesis is focused on forecasting methods and their comparison according to accuracy indicators. Forecast methods were utilized for building a forecast model of demand in texture industry. Usefulness of the thesis comes from forecasting an amount of demand in the future, which can be used by sellers, manufacturers and others impacted by amount of demand in textile industry. Thesis contains general reccomendations on forecasting process and helps with choice of appropriate methods of forecast.
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Anomaly Detection in Generated Incident Ticket Volumes
Šurina, Timotej ; Rychlý, Marek (referee) ; Trchalík, Roman (advisor)
Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.
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Holt-Winters method for exponential smoothing
Koritarová, Lenka ; Cipra, Tomáš (advisor) ; Prášková, Zuzana (referee)
"his thesis de-ls with the methods of exponenti-l smoothingF et (rst the prin iE ples of exponenti-l smoothing -re expl-inedF e fo us on -si -ppro- hesX sinE gleD dou le smoothing -nd the rolt¡s methodF "hese pro edures -re suit- le for the modeling time series without se-son-l omponentF rowever in pr- ti e there -re frequent time series with se-son-lityF por su h time series the roltE inter¡s method is usedF "his method is -sed just on the prin iples of exponenti-l smooE thingF sn the l-st p-rt of this thesisD there is demonstr-ted using this methods on re-l d-t-F
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Selected methods of time series analysis with STATISTICA
Indrová, Magdalena ; Hudecová, Šárka (advisor) ; Zichová, Jitka (referee)
This work deals with the use of STATISTICA software for the basic analysis of time series. The thesis is focused on time series decomposition, mainly on the trend elimination. First, the basic methods of the analysis are described theoretically, namely, trend modeling using mathematical curves (polynomial, exponential, logistic and Gompertz) and adaptive approach (moving averages, simple exponential smoothing and Holt's method). These methods are then applied to three selected data sets (unnamed bank's balance sheet from 1998 to 1993, ship construction trends between 1820 and 1997, and CZK/EUR Exchange rate from 1998 to 2012). All analytical procedures are described in detail and individual program outputs are thoroughly explained and commented.
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Some problems of exponential smoothing
Čurda, David ; Hanzák, Tomáš (advisor) ; Komárek, Arnošt (referee)
In this work the several exponential smoothing type methods are briefly described, which are often used to smoothing and forecasting in the time series. Selected problems, that occur in described methods, are presented and in some cases there are the suggestions to their solution, which should tend to more suitable smoothing or to the better forecasts. It's shown how the methods are applied on different data and how the forecasts differ from each other. In conclusion the quality of modifications is evaluated.
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The Forecasting Model of Demand in the Textile Industry
Kunc, Tomáš ; Oulehla, Jiří (referee) ; Luňáček, Jiří (advisor)
Thesis is focused on forecasting methods and their comparison according to accuracy indicators. Forecast methods were utilized for building a forecast model of demand in texture industry. Usefulness of the thesis comes from forecasting an amount of demand in the future, which can be used by sellers, manufacturers and others impacted by amount of demand in textile industry. Thesis contains general reccomendations on forecasting process and helps with choice of appropriate methods of forecast.
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Methods for periodic and irregular time series
Hanzák, Tomáš
Title: Methods for periodic and irregular time series Author: Mgr. Tomáš Hanzák Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Tomáš Cipra, DrSc. Abstract: The thesis primarily deals with modifications of exponential smoothing type methods for univariate time series with periodicity and/or certain types of irregularities. A modified Holt method for irregular times series robust to the problem of "time-close" observations is suggested. The general concept of seasonality modeling is introduced into Holt-Winters method including a linear interpolation of seasonal indices and usage of trigonometric functions as special cases (the both methods are applicable for irregular observations). The DLS estimation of linear trend with seasonal dummies is investigated and compared with the additive Holt-Winters method. An autocorrelated term is introduced as an additional component in the time series decomposition. The suggested methods are compared with the classical ones using real data examples and/or simulation studies. Keywords: Discounted Least Squares, Exponential smoothing, Holt-Winters method, Irregular observations, Time series periodicity
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Probability forecast in exponential smoothing models
Viskupová, Barbora ; Hudecová, Šárka (advisor) ; Cipra, Tomáš (referee)
This thesis deals with the use of statistical state space models of exponential smooth- ing for estimating the conditional probability distribution of future values of time series. This knowledge allows calculation of interval predictions, not only point forecasts. Meth- ods of exponential smoothing are described and set into the context of state space models. Analytical and simulation methods used in the calculation of interval predictions are presented, in particular simulations based on assumption of normality, bootstrap method or estimated parametric model. The methods are applied to simulated as well as real data and their results are compared. 1
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Anomaly Detection in Generated Incident Ticket Volumes
Šurina, Timotej ; Rychlý, Marek (referee) ; Trchalík, Roman (advisor)
Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.
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