Performance of ensemble forecasting tools for analysis Turkish consumer price index

Download

index.pdf

Date

2018

Author

Aydemir, Petek

Metadata

Show full item record

Item Usage Stats

419
views

112
downloads

Major challenge in time series analysis is to get reasonably accurate forecasts of the future data from the analysis of the previous records. Accurate forecasting of inflation has great importance in the market economies, the policymakers and the monetary system since the inflation rate determines the cost and standard of living. Also, it affects the decision on investments. In Turkey, the inflation rate is measured by the consumer price index (CPI). There exist many methods to predict the future values of the CPI. In this study, six individual models were applied to forecast the Turkish CPI. Those are Seasonal Autoregressive Integrated Moving Average Model with Exogeneous variables (SARIMAX), Holt-Winters Exponential Smoothing, Trigonometric Exponential Smoothing State Space model with Box-Cox transformation, ARMA errors, Trend and Seasonal Components (TBATS) model, Artificial Neural Network (ANN), Theta Model, Seasonal Trend Decomposition with Loess (STL). Then, ensemble model was constructed to improve the forecast performance. Ensemble model is the combination of several forecasting models to improve the performance of the forecast. The forecast accuracy of all models is evaluated by the Root Mean Square Error and Mean Absolute Percentage Error. Our findings show that SARIMAX(4,1,4)(2,0,1)x12 and ensemble model composed of auto.arima and neural network produce the best forecasts for 12 month Turkish CPI.

Subject Keywords

Inflation (Finance), Time-series analysis.

URI

http://etd.lib.metu.edu.tr/upload/12622509/index.pdf
https://hdl.handle.net/11511/27473

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Multiresolution analysis of S&P500 time series KILIC, Deniz Kenan; Uğur, Ömür (2018-01-01) Time series analysis is an essential research area for those who are dealing with scientific and engineering problems. The main objective, in general, is to understand the underlying characteristics of selected time series by using the time as well as the frequency domain analysis. Then one can make a prediction for desired system to forecast ahead from the past observations. Time series modeling, frequency domain and some other descriptive statistical data analyses are the primary subjects of this study: i...
Bayesian modelling for asymmetric multi-modal circular data Kılıç, Muhammet Burak; Kalaylıoğlu Akyıldız, Zeynep Işıl; Sengupta, Ashis; Department of Statistics (2015) In this thesis, we propose a Bayesian methodology based on sampling importance re-sampling for asymmetric and bimodal circular data analysis. We adopt Dirichlet process (DP) mixture model approach to analyse multi-modal circular data where the number of components is not known. For the analysis of temporal circular data, such as hourly measured wind directions, we join DP mixture model approach with circular times series modelling. The approaches are illustrated with both simulated and real life data sets. ...
Cooperative interval games Alparslan Gök, Sırma Zeynep; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009) Interval uncertainty affects our decision making activities on a daily basis making the data structure of intervals of real numbers more and more popular in theoretical models and related software applications. Natural questions for people or businesses that face interval uncertainty in their data when dealing with cooperation are how to form the coalitions and how to distribute the collective gains or costs. The theory of cooperative interval games is a suitable tool for answering these questions. In this ...
REACTIVE POINT PROCESSES: A NEW APPROACH TO PREDICTING POWER FAILURES IN UNDERGROUND ELECTRICAL SYSTEMS Ertekin Bolelli, Şeyda; Mccormick, Tyler H. (2015-03-01) Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturatin...
Macroeconomic announcements and intraday stock market volatility Yılmaz, Berna Nisa; Danışoğlu, Seza; Department of Financial Mathematics (2017) This study examines the effects of interest and inflation rate announcements on stock market volatility by using a standard event study methodology. The BIST-30 Index volatility is modelled and forecasted by the multiplicative component GARCH model. This is one of the first studies where the announcement effects are analyzed on the basis of volatility forecasts produced by the multiplicative component GARCH. The announcement effects are observed clearly with the advantage of using high-frequency data. While...

Citation Formats

P. Aydemir, “Performance of ensemble forecasting tools for analysis Turkish consumer price index,” M.S. - Master of Science, Middle East Technical University, 2018.