ETD Collection

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Subject: search.filters.subject.Time-series analysis

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    Empirical comparison of the performance of structural time series methods in forecasting daily temperature: case of Johannesburg, South Africa.
    (2018) Memela, Thokozani Eugen
    TheAimofthisstudywastocomparetheforecastingaccuracyofexponentialsmoothing methods and unobserved component methods in forecasting Johannesburg daily temperature. The other objective of this study was to assess the effect of aggregating daily temperature to monthly temperature has on forecasting accuracy of the two structural time series methods. The Johannesburg daily temperature time series used spanned from 01 March 2007 to 31 March 2017. An extension of Holt-Winters model know as TBATS(Trigonometric Fourier representations, Box-Cox transformations, ARMA errors, Trend, and Seasonal component) by De Livera et al. (2011) was found to be more accurate to forecasts Johannesburg daily temperature. This model had high accuracy in Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Thestudyalsofoundthatthetwostructuraltimeseriesmethodsweresensitivetotime series classical components present in the data. The monthly temperature data was much smoother than daily temperature data. The two structural time series models used were much accurate in identifying the classical components of a smoother time series. This resulted in much accurate forecast. Holt-Winters additive seasonal model was found to be more accurate to forecast Johannesburg monthly temperature. This model out-performed local linear trend plus Fourier seasonal unobserved component model with high accuracy in Mean Absolute Error (MAE), Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE).
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    The stock market as a leading indicator of economic activity: time-series evidence from South Africa
    (2016) Sayed, Ayesha
    Several studies have assessed the forward-looking characteristic of share prices and confirmed their resultant capability as leading indicators of economic activity, especially in advanced economies. Contention however exists when evaluating the role of stock markets as leading indicators for less developed countries. This study examines the validity of the stock market as a leading indicator of economic activity in South Africa using quarterly time-series data for the period January 1992 to June 2014. Causality and cointegration between the JSE All Share Index against Real GDP and Real Industrial Production is evaluated by employing Granger-causality tests and the Johansen cointegration procedure. The empirical investigation indicates that unidirectional causality exists between the nominal and real stock indices and economic activity in South Africa, and confirms a long-run relationship between the JSE and GDP and Industrial Production. Therefore, similar to the study by Auret and Golding (2012), in a South African context, the stock market is in fact a leading indicator of economic activity.
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    The relationship between pollen rain, vegetation, climate, meteorological factors and land-use in the PWV, Transvaal
    (1991) Cadman, Ann
    A two-year analysis of pollen rain was conducted in the Pretoria-Witwatersrand-Vereeniging district of the Transvaal, South Africa. Poaceae WaS the major component of the pollen assemblage, comprising 52% regionally. Of the total pollen count, 58.8% was non-seasonal and present throughout the year. During the analysis it became apparent that fungal spores dominated the atmospheric content, accounting for 94% of total airspora, considered here to incl ude pollen and fUngal spores.[Abbreviated Abstract. Open document to view full version].