The effect of temporal aggregation on univariate time series analysis

Download

index.pdf

Date

2010

Author

Sarıaslan, Nazlı

Metadata

Show full item record

Item Usage Stats

369
views

101
downloads

Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.

Subject Keywords

Time-series analysis.

URI

http://etd.lib.metu.edu.tr/upload/12612528/index.pdf
https://hdl.handle.net/11511/20165

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A Computational approach to detect inhomogeneities in time series data Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci; Department of Statistics (2017) Detection of possible inhomogeneity within a series is an important problem in time series data. There are many sources from which inhomogeneity can be originated such as mean shift, variance and trend change, gradual change, or sudden decrease or increase in time series. Since time series has many application areas, the detection of changepoints should be investigated before conducting any analysis. Available methods have certain drawbacks that may lead to unreliable inferences. These include the need of i...
A temporal neuro-fuzzy approach for time-series analysis Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003) The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may ...
The forecast performances of classical time series models and machine learning algorithms on bitcoin series using exogenous variables Doğan, Sevilay; Yozgatlıgil, Ceylan; Department of Statistics (2022-11-30) Time series analysis importantly gives insight into what happens to a time series on any subject for days, weeks, months or years. Bitcoin is the most popular technology since it has exclusive attention in economics and finance. In this study, some of the approaches are investigated about forecasting and modeling the most popular cryptocurency bitcoin prices.The performance of classical time series methods and machine learning algorithms are compared in the study. As classical time series models, Autoregres...
A comparative study of autoregressive neural network hybrids Taşkaya Temizel, Tuğba (2005-06-01) Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear componen...
Probabilistic Forecasting of Multiple Time Series with Single Recurrent Neural Network TOPALLAR, SARP TUĞBERK; Yozgatlıgil, Ceylan; Department of Scientific Computing (2022-9-20) Time series forecasting can be summarized as predicting the future values of a sequence indexed by timestamps based on the past records of that sequence. Optimal or near-optimal resource allocation requires accurate predictions into the future. The study presents investigation performed on both classical methods and more contemporary methods from the literature. The classical methods studied are Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) and Seasonal-Trend decomposition us...

Citation Formats

N. Sarıaslan, “The effect of temporal aggregation on univariate time series analysis,” M.S. - Master of Science, Middle East Technical University, 2010.