Forecasting with auxiliary information in forecasts using multivariate singular spectrum analysis

The internet gives us free access to a variety of published forecasts. Motivated by this increasing availability of data, we seek to determine whether there is a possibility of exploiting auxiliary information contained within a given forecast to generate a new and more accurate forecast. The proposed theoretical concept requires a multivariate model which can consider data with different series lengths as forecasts are predictions into the future. Following applications which consider published forecasts generated via unknown time series models and forecasts from univariate models, we achieve promising results whereby the proposed multivariate approach succeeds in extracting the auxiliary information in a given forecast for generating a new and more accurate forecast, along with statistically significant accuracy gains in certain cases. In addition, the impact of filtering and the use of Google Trends within the proposed methodology is also considered. Overall, we find conclusive evidence which suggests a sound opportunity to exploit the forecastability of auxiliary information contained within existing forecasts

Singular Spectrum Analysis: Methodology and Comparison

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.ARAR algorithm; Box-Jenkins SARIMA models; Holt-Winter algorithm; singular spectrum analysis (SSA); USA monthly accidental deaths series

Forecasting with Big Data: A Review

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data

Forecasting UK Consumer Price Infation using Infation Forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modelling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modelling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework

Forecasting energy data with a time lag into the future and Google trends

This paper presents a new idea for a forecasting approach which seeks to exploit the information contained within US EIA energy forecasts and related Google trends data for generating a new and improved forecast. The novel forecasting approach can be exploited by using a multivariate system which can consider data with different series lengths and a time lag into the future. Using real historical data, an official forecast for the same variable, and Google Trends search data, we illustrate the possibility of generating a comparatively more accurate forecast for an energy-related variable. The accuracy of the newly generated forecasts are evaluated by comparing with the actual observations and the official forecast itself. We find that the novel forecasting idea cangenerate promising results which call for further in-depth research into developing and improving this multivariate forecasting approach

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

Predicting Global Temperature Anomaly: A Definitive Investigation Using an Ensemble of Twelve Competing Forecasting Models

In this paper we analyze whether (anthropometric) CO$_2$ can forecast global temperature anomaly (GT) over an annual out-of-sample period of 1907-2012, which corresponds to an initial in-sample of 1880-1906. For our purpose, we use 12 parametric and nonparametric univariate (of GT only) and multivariate (including both GT and CO$_{2}$) models. Our results show that the Horizontal Multivariate Singular Spectral Analysis (HMSSA) techniques (both Recurrent (-R) and Vector (-V)) consistently outperform the other competing models. More importantly, from the performance of the HMSSA-V model we find conclusive evidence that CO$_2$ can forecast GT, and also predict its direction of change. Our results highlight the superiority of the nonparametric approach of SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian

Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods

This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development and has shown much potential. Stochastic characteristics of stock market data are analyzed and found to be definitely not Gaussian, but instead better fit to a generalized t-distribution. Probability distribution models are validated with goodness-of-fit tests. For analysis, stock data is segmented into non-overlapping time “windows” to support unconditional statistical evaluation. Univariate and multivariate ARIMA and SSA time series models are evaluated for independence. ARIMA models are found to be independent, but SSA models are not able to reach independence. Statistics for out-of-sample forecasts are computed for every stock in every window, and multivariate-univariate confidence interval shrinkages are examined. Results are compared for univariate, bivariate, and trivariate combinations of highly-correlated stocks. Effects are found to be mixed. Bivariate modeling and forecasting with three different covariates are investigated. Examination of results with covariates of trading volume, principal component analysis (PCA), and volatility reveal that PCA exhibits the best overall forecasting accuracy in the entire field of investigated elements, including univariate models. Bivariate-PCA structures are applied in a back-testing environment to evaluate economic significance and robustness of the methods. Initial results of back-testing yielded similar results to those from earlier independent testing. Inconsistent performance across test intervals inspired the development of a second technique that yields improved results and positive economic significance. Robustness is validated through back-testing across multiple market trends

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Forecasting with DSGE models

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-step ahead forecasts. In the empirical analysis, we examine the forecasting performance of the New Area-Wide Model (NAWM) that has been designed for use in the macroeconomic projections at the European Central Bank. The forecast sample covers the period following the introduction of the euro and the out-of-sample performance of the NAWM is compared to nonstructural benchmarks, such as Bayesian vector autoregressions (BVARs). Overall, the empirical evidence indicates that the NAWM compares quite well with the reduced-form models and the results are therefore in line with previous studies. Yet there is scope for improving the NAWM’s forecasting performance. For example, the model is not able to explain the moderation in wage growth over the forecast evaluation period and, therefore, it tends to overestimate nominal wages. As a consequence, both the multivariate point and density forecasts using the log determinant and the log predictive score, respectively, suggest that a large BVAR can outperform the NAWM. JEL Classification: C11, C32, E32, E37Bayesian inference, DSGE Models, euro area, forecasting, open-economy macroeconomics, Vector autoregression