1. Introduction#
This book is a comprehensive guide designed to accelerate the learning process for those eager to delve into the world of time series analysis. Time series analysis is a fundamental aspect of various domains, including economics, finance, and weather forecasting, among others, where data evolves over time, enabling the extraction of significant insights for informed future decisions.
At the core, a time series is a sequential set of data points collected or recorded at specific time intervals. The uniqueness of time series data lies in its temporal dependency, where each data point is influenced by its predecessors, setting it apart from traditional cross-sectional data analysis.
Our guide navigates through the crucial components of time series data such as trends, seasonality, cyclical patterns, and irregularities, ensuring a well-rounded understanding. Essential aspects such as smoothing techniques, stationarity, forecasting methods, and seasonal decomposition are meticulously explored to equip the readers with the necessary tools for practical application.
We also shed light on the real-world applicability of time series analysis, covering diverse areas such as stock price prediction, sales forecasting, and weather prediction, among others. Furthermore, this book provides insights into the software and tools integral to time series analysis, including popular libraries in R and Python, and specialized tools like Tableau and Power BI.
Throughout this book, readers will encounter the challenges prevalent in time series analysis, such as noise, outliers, and model selection, ensuring a well-prepared approach to practical challenges.
In essence, this book aims to serve as a valuable resource for junior members and enthusiasts looking to harness the power of time series analysis for insightful data interpretation and prediction in various realms of application.
following packages and modules imported in the notebook:
dartsgoogle.colabkerasmatplotlibneuralprophetnumpypandaspastaspmdarimaprophetrequestsscipysklearnsktimestatsmodelstensorflowtsfreshtslearnurllib
Note: that the script also extracted some specific modules, classes, and functions, such as Sequential, LSTM, ARIMA, and plot_acf, among others. These are parts of the larger libraries/packages and might not be standalone packages.