Predicting the demise of retail bookstores: a time series forecasting

Results and Discussion “The internet is killing retail. Bookstores are just the first to go.” -- quoted in the NYT article. Retail bookstores are in death row; looks like it's just a matter of time for those to be history. eBooks are partly to blame, but with eBook sales leveling off recently, the remaining affect … Continue reading Predicting the demise of retail bookstores: a time series forecasting

Time Series Analysis & Forecasting of New Home Sales

Table of Contents IntroductionObjectivesData & Methods Results 4.1 Exploratory Data Analysis (EDA) 4.2 Forecasting 4.2.1 Input data & decomposition 4.2.2 Forecasting with HW Exponential Smoothing 4.2.3 Forecasting with ETS 4.2.4 Forecasting with ARIMA Discussion & Conclusion 5.1 Model evaluation 5.2 General conclusions 5.3 Discussion 1. Introduction New house construction & sales plays a significant role … Continue reading Time Series Analysis & Forecasting of New Home Sales

Benchmark forecasting example: Japanese population by 2030

Disclaimer I'm not going to discuss time series and forecasting theories, not here, not anytime in the future in this writing series. I am deliberately avoiding any and all theories, as much as possible (if you are curious why, I wrote about it in here). If you are interested in theories, there are plenty of materials … Continue reading Benchmark forecasting example: Japanese population by 2030

Learning Data Science by examples rather than theories

What is a good starting point in data science learning process? Traditionally we start with theories, then the mechanics and finally make hands dirty. This rapidly kills excitement by exponentially loosing interest, and at some point get out of track. This happens more often then not before doing any of the problem solving work we … Continue reading Learning Data Science by examples rather than theories