Further material on TSA

Useful material to deepen technical, statistical and applicative aspects of TSA methods:

[1] is a very good reference for studying in detail the technical aspects of the classical statistical methods of TSA, there is also the online version.
[2] is the book from Box & Jenkins, highly technical, it covers modern topics and classical models, and it allows you to study the techniques in depth.
[3] and [4] are books with also examples in R, very useful to see practical examples of applications.
[5] Machine Learning Mastery is a very good reference to both theoretical and practical examples, with also applications using more complex techniques, such as RNN.
[6], [7], and [8] are references about time series techniques applied to finance.
[9] is a paper about the last Makridakis competition, which is a open competition to evaluate and compare the accuracy of different time series forecasting methods. It is very useful to assess which is the currently best TSA performing techniques (e.g., deep learning vs statistical techniques).
[10] forecasting multiple time series (demand forecasting) at the same time
[11] comparazione tra metodi statistici e deep learning

References [1] Forecasting: Principles and Practice, 3rd edition (https://otexts.com/fpp3/) [2] Box, George EP, et al. Time series analysis: forecasting and control. John Wiley & Sons, 2015. [3] Cryer, Jonathan D., and Kung-Sik Chan. Time series analysis: with applications in R. Vol. 2. New York: Springer, 2008. [4] Shumway, Robert H., David S. Stoffer, and David S. Stoffer. Time series analysis and its applications. Vol. 3. New York: springer, 2000. [5] https://machinelearningmastery.com/category/time-series/ [6] Chan, Ngai Hang. Time series: applications to finance. John Wiley & Sons, 2004. [7] Time Series for Macroeconomics and Finance http://www.smartquant.com/references/TimeSeries/ts13.pdf [8] https://towardsdatascience.com/time-series-analysis-tutorial-using-financial-data-4d1b846489f9 [9] https://www.sciencedirect.com/science/article/pii/S0169207019301128 [10] https://towardsdatascience.com/forecast-multiple-time-series-like-a-master-1579a2b6f18d [11] https://towardsdatascience.com/time-series-forecasting-deep-learning-vs-statistics-who-wins-c568389d02df

emanueledellavalle / streaming-data-analytics