Basic Singular Spectrum Analysis and Forecasting with R

Singular Spectrum Analysis (SSA) as a tool for analysis and forecasting of time series is considered. The main features of the Rssa package, which implements the SSA algorithms and methodology in R, are described and examples of its use are presented. Analysis, forecasting and parameter estimation are demonstrated by means of case study with an accompanying code in R

Short-Term Volatility Curve Predictions Using Singular Spectrum Analysis

This project aims to produce accurate volatility forecasts, using high-frequency financial time series data. The primary mathematical methods used are Functional Data Analysis, time series analysis techniques such as Autoregressive Models and a comparison between Multi-variate and Uni-variate Singular Spectrum Analysis. These results aim to be useful for financial risk quantification

Shaped extensions of singular spectrum analysis

Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows application of the method to the data given on a circle or on a cylinder, e.g. cylindrical projection of a 3D ellipsoid. The constructed Shaped SSA method with planar or circular topology is able to produce low-rank approximations for images of complex shapes. Together with Shaped SSA, a shaped version of the subspace-based ESPRIT method for frequency estimation is developed. Examples of 2D circular SSA and 2D Shaped ESPRIT are presented

Forecasting Consumer Price Index Expenditure Inflation for Food Ingredients Using Singular Spectrum Analysis

Inflation is an economic problem that significantly impacts the macro economy and people's real income if it occurs continuously. South Sulawesi Province often experienced significant inflation fluctuations during 2005-2019. In 2015, inflation in South Sulawesi reached 3.32%, ranking the highest in Eastern Indonesia. Ten food ingredients played an essential role in influencing inflation that year. However, until now, research on forecasting Consumer Price Index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method has never been carried out. The novelty in this research lies in using the Singular Spectrum Analysis method, which provides a new contribution to forecasting inflation trends in South Sulawesi and deepens understanding of regional inflation problems. This research aims to forecast consumer price index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method. This research used CPI expenditure inflation data for food ingredients from the official website of the Central Statistics Agency of South Sulawesi for the monthly period from January 2014 - June 2022. The forecasting results show that the lowest inflation rate is predicted to occur in December 2022 at -0,12%, while the highest level is expected to be reached in May 2023 at 0.43%. Furthermore, the mean absolute percentage error value of 3.54% indicates that the forecasting model has a very good level of accuracy. The results of this forecasting have the potential to be used by economic policymakers in South Sulawesi in designing more effective policies to overcome the problem of inflation, especially in the food ingredients and its impact on society. The practical implications of this research can help improve regional economic stability and community welfare

Прогнозирование тока на основании данных энергомонитора

Материалы XII Междунар. науч.-техн. конф. (науч. чтения, посвящ. П. О. Сухому), Гомель, 22–23 нояб. 2018 г

A Singular Spectrum Analysis Technique to Electricity Consumption Forecasting

Singular Spectrum Analysis (SSA) is a relatively new and powerful nonparametric tool for analyzing and forecasting economic data. SSA is capable of decomposing the main time series into independent components like trends, oscillatory manner and noise. This paper focuses on employing the performance of SSA approach to the monthly electricity consumption of the Middle Province in Gaza Strip\Palestine. The forecasting results are compared with the results of exponential smoothing state space (ETS) and ARIMA models. The three techniques do similarly well in forecasting process. However, SSA outperforms the ETS and ARIMA techniques according to forecasting error accuracy measures