Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR tech

Inflation tends to be a relatively persistent process, which means that current and past values should be helpful in forecasting future inflation. Applying this intuition, we construct a basic stochastic model which exploits information embedded in past values of Ghana's inflation data. Therefore the aim of this study is not to identify the drivers of Ghana's inflation, but to identify and forecast with the best predicting model for Ghana's inflation, based on the stochastic mechanisms that governs Ghana's inflation series. We then use this identified model to forecast one-year-ahead (that is, 2018) inflation using past lags, specifically, monthly inflation, from January 2010 to September 2017. Per our forecast, the Bank of Ghana's aim of hitting a single digit for the year 2018 will not be realized, even though the year closes with a lower inflation than what it began with.