Performance of ensemble forecasting tools for analysis Turkish consumer price index

Download
2018
Aydemir, Petek
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.

Suggestions

Consensus clustering of time series data
Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014)
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
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...
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...
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...
Prediction Model Selection with Frequency Check on Decomposed Time Series
Büyükşahin, Ümit Çavuş; Ertekin Bolelli, Şeyda (2019-08-22)
High prediction accuracies at time series modeling and forecasting is of the utmost importance for a variety of application domains. Various time series prediction methods exist that use linear and nonlinear models separately or combination of both. These methods highly increase prediction performance results when they are applied on a many number of stationary components obtained by more sophisticated decomposition techniques. Although these stationary components are easily predictable, they each have diff...
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.