Analysis of gold price data from January 2000 to May 2024 and forecast for the next 10-months.

The aim of this project is to determine the most suitable trend model for an AUD100 dataset representing the returns of a share market trader’s investment portfolio. Various trend models including linear, quadratic, cosine, cyclical, and seasonal trends will be explored, and the best fitting model identified. Additionally, this model will be used to predict the returns for the next 5 trading days.

In this study, We aim to analyse and forecast horizontal solar radiation reaching the Earth’s surface at specific global locations using monthly average solar radiation and precipitation data spanning from January 1960 to December 2014. The goal is to provide accurate two-year ahead forecasts based on the most suited model. We’ll explore various time series regression methods, including dLagM, exponential smoothing and the corresponding state-space models and measure performance based on MASE, AIC and residual check.

In this analysis, we explore the ASX dataset which features monthly averages of ASX all Oridinaries (Ords) Price Index, Gold Price (AUD), Crude Oil(Brent,USD/bbl) and Copper(USD/tonne) starting from January 2004. Aim of this analysis: Determine the presence of nonstationarity within the dataset.Investigate the impact of individual time series components. Identify the most suitable distributed lag model for accurate ASX price index prediction