Does AI Research Aid Prediction? A Review and Evaluation

Despite the increasing application of Artificial Intelligence (AI) techniques to business over the past decade, there are mixed views regarding their contribution. Assessing the contribution of AI to business has been difficult, in part, due to lack of evaluation criteria. In this study, we identified general criteria for evaluating this body of fiterature. Within this framework, we examined applications of AI to business forecasting and prediction. For each of the seventy studies located through our search, we evaluated how effectively the proposed technique was compared with alternatives (effectiveness of validation) as well as how well the technique was implemented (effectiveness of implementation). We concluded that by using acceptable practice and providing validated comparisons, 31% (22) of the studies contributed to our knowledge about the applicability of the AI techniques to business. Of these twenty-two studies, twenty supported the potential of AI in forecasting. This small number of studies indicates a need for improved research in this area

NPL forecasting under a fourier residual modified model: An empirical analysis of an unsecured consumer credit provider in South Africa

Forecasting nonperforming loans (NPLs) is a primary objective for credit providers. NPL forecasts assist in financial budgeting and provisioning for bad debts. The difficulty in accurately identifying the determinants of domestic NPLs has led to a review of time series forecasting techniques. This dissertation explores whether a forecasting model combining a traditional time series approach with a Fourier series residual modification technique performs well in projecting NPLs. It also seeks to establish if selecting an adequate time series model before modifying its residual terms is of benefit. Using the data of an unsecured consumer credit provider in South Africa, the in-sample and out-of-sample performance for a seasonal time series model and residual modified model were evaluated. The results demonstrate that a time series model performs well but the out-of-sample forecasting errors may be reduced by including the lowest Fourier frequencies to modify the residual terms

Time Series Forecasting: An Application to Balance Sheet

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internal Capital Adequacy Assessment Process (ICAAP) provides a qualitative and quantitative assessment of capital risks to which banking institutions are exposed to in their activity. Caixa Geral de Dep´ositos (CGD) is a relevant player in the Portuguese banking system, and as such it has to perform an ongoing review of ICAAP exercise to evaluate its ability to identify, assess, mitigate and report on its risks. In order to properly quantify all the risks the institution is exposed to, several models need to be developed to help estimate the amount of capital that is needed to cover potential unexpected losses arising from each type of risk. Given the European and Portuguese guidelines these models also have to comply with certain requirements defined by Banco de Portugal, European Central Bank (ECB) and European Banking Authority (EBA) regarding ICAAP exercise. One of the risks CGD is exposed to is the risk of an unfavourable evolution of the main credit items in its Balance Sheet and as such, it is necessary to estimate the evolution of certain credit items (in terms of their volumes and spread rates). These estimations are needed for relevant segments such as housing credit, consumer and other credit, public sector credit, real estate activities credit, non-financial corporate credit and term and sight deposits. To estimate the evolution of these balance sheet items, a robust and reliable methodology must be applied, so that it can truly help strategic decision-making process over a horizon period of three years and the appropriate amount of capital can be allocated. At CGD, Balance Sheet credit volumes and spread rates had been being estimated through multiple linear regressions to which macroeconomic indicators are added as explanatory variables. The problem with this methodology, is that these type of dependent and explanatory financial variables are usually in the form of time series, indicating the existence of correlation between any observation and the previous one, meaning that there is dependence on the past historical information. Applying multiple linear regressions to this type of data leads to poor statistical results and to the non-compliance of all the statistical assumptions linear regressions must respect. Within this context, the need to turn to a more adequate and robust methodology became more evident and time series forecasting appeared to be the so long needed solution that would allow to reach reliable statistical results. Time series forecasting is commonly used in economics and finance, denoting a robust technique to predict macroeconomic variables representing a feasible approach to apply to estimate CGD’s main credit volumes and spread rates of the balance sheet. In this project, we investigate the estimation of Balance Sheet credit volumes and spreads rates using time series forecasting aiming to assess the models suitability to quantify the risk of unfavourable balance sheet evolution of the main credit segments. The models proposed for this purpose, are the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) models. The results obtained proved to have robust statistical results and high performance, which were verified by analysing residuals statistical behaviour and key performance indicators such as the Mean Squared Error (MSE) and the Akaike Information Criterion (AIC) of the final models selected for each target variable

Memory Diagnostic in Time Series

The objectives of this thesis is to evaluate the reliability of different periodogram-based estimation techniques and their non-spectral alternatives, implemented in the free software environment for statistical computing and graphics R, in distinguishing time series sequences with different memory processes, specifically to discriminate (1) two different classes of persistent signals within fractal analysis, fractional Brownian motions (fBm) and fractional Gaussian noises (fGn) (2) nonstationary and stationary ARFIMA (p,d,q) processes as well as (3) short- and long-term memory properties of the latter, and to assess the accuracy of the corresponding estimates. After a brief introduction into time- and frequency-domain analyzes fundamental concepts such as the ARFIMA methodology and fractal analysis for modeling and estimating long-(LRD) and short-range dependence (SRD) as well as (non)stationary of time series are presented. Furthermore, empirical studies utilizing time series analysis of long memory processes as diagnostic tools within psychological research are demonstrated. Three simulation studies designed to solve the abovementioned methodological problems represent the main field of this thesis, i.e., the reliable identification of different memory as well as specific statistical properties of ARFIMA and fractal time series and the assessment of estimation accuracy of the procedures under evaluation, and thus, based on the empirical findings, recommending the most reliable procedures for the task at hand

Statistical modelling and estimation of solar radiation.

M.Sc. University of KwaZulu-Natal, Durban 2014.Solar radiation is a primary driving force behind a number of solar energy applications such as photovoltaic systems for electricity generation amongst others. Hence, the accurate modelling and prediction of the solar flux incident at a particular location, is essential for the design and performance prediction of solar energy conversion systems. In this regard, literature shows that time series models such as the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) stochastic models have considerable efficacy to describe, monitor and forecast solar radiation data series at various sites on the earths surface (see e.g. Reikard, 2009). This success is attributable to their ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. On the other hand at the top of the atmosphere, there are no such conditions and deterministic models which have been used successfully to model extra-terrestrial solar radiation. One such modelling procedure is the use of a sinusoidal predictor at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle. We combine this deterministic model component and SARIMA models to construct harmonically coupled SARIMA (HCSARIMA) models to model the resulting mixture of stochastic and deterministic components of solar radiation recorded at the earths surface. A comparative study of these two classes of models is undertaken for the horizontal global solar irradiance incident on the solar panels at UKZN Howard College (UKZN HC), located at 29.9º South, 30.98º East with elevation, 151.3m. The results indicated that both SARIMA and HCSARIMA models are good in describing the underlying data generating processes for all data series with respect to different diagnostics. In terms of the predictive ability, the HCSARIMA models generally had a competitive edge over the SARIMA models in most cases. Also, a tentative study of long range dependence (long memory) shows this phenomenon to be inherent in high frequency data series. Therefore autoregressive fractionally integrated moving average (ARFIMA) models are recommended for further studies on high frequency irradiance.Please refer to page xii of thesis for abbreviations that appear in the abstract

Managing Commodity Risks in Highway Contracts: Quantifying Premiums, Accounting for Correlations Among Risk Factors, and Designing Optimal Price-Adjustment Contracts

It is a well-known fact that macro-economic conditions, such as prices of commodities (e.g. oil, cement and steel) affect the cost of construction projects. In a volatile market environment, highway agencies often pass such risk to contractors using fixed-price contracts. In turn, the contractors respond by adding premiums in bid prices. If the contractors overprice the risk, the price of fixed-price contract could exceed the price of the contract with adjustment clauses. Consequently, highway agencies have the opportunity to design a contract that not only reduces the future risk of exposure, but also reduces the initial contract price. The main goal of this dissertation is to investigate the impact of commodity price risk on construction cost and the optimal risk hedging of such risks using price adjustment clauses. More specifically, the objective of the dissertation is to develop models that can help highway agencies manage commodity price risks. In this dissertation, a weighted least square regression model is used to estimate the risk premium; both univariate and vector time series models are estimated and applied to simulate changes in commodity prices over time, including the effect of correlation; while the genetic algorithm is used as a solution approach to a multi-objective optimization formulation. The data set used in this dissertation consists of TxDOT bidding data, market-based data including New York Mercantile Exchange (NYMEX) future options data, and Engineering News-Record (ENR) material cost index data. The results of this dissertation suggest that the optimal risk mitigation actions are conditional on owners' risk preferences, correlation among the prices of commodities, and volatility of the market

Modelling computer network traffic using wavelets and time series analysis

Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fashion. Thus in this dissertation the activity of building time series models for IoT network traffic is undertaken. In the work that follows a broad review of the historical development of network traffic modelling is presented tracing a path that leads to the use of time series analysis for the said task. An introduction to time series analysis is provided in order to facilitate the theoretical discussion regarding the feasibility and suitability of time series analysis techniques for modelling network traffic. The theory is then followed by a summary of the techniques and methodology that might be followed to detect, remove and/or model the typical characteristics associated with network traffic such as linear trends, cyclic trends, periodicity, fractality, and long range dependence. A set of experiments is conducted in order determine the effect of fractality on the estimation of AR and MA components of a time series model. A comparison of various Hurst estimation techniques is also performed on synthetically generated data. The wavelet-based Abry-Veitch Hurst estimator is found to perform consistly well with respect to its competitors, and the subsequent removal of fractality via fractional differencing is found to provide a substantial improvement on the estimation of time series model parameters