Time-Series-Analysis-and-Forecasting-with-Python 📈📉📊⏰

🤘 Welcome to the comprehensive guide on Time-Series Analysis and Forecasting using Python 👨🏻‍💻. This repository is designed to equip you with the knowledge, tools, and techniques to tackle the challenges of analyzing and forecasting time-series data. Whether you're a beginner curious about the basics of time-series analysis or an advanced practitioner aiming to delve into the depths of forecasting models, this guide has something for you🫱🏻‍🫲🏼.

🚀 The contents are structured to provide a logical progression, starting with an introduction to the concepts and practices of time-series analysis, followed by data visualization techniques, exploratory data analysis (EDA), and more in-depth data analysis. We then transition 💥 into various forecasting methodologies, including classical statistical models, cutting-edge deep learning approaches, and the application of Facebook's Prophet tool for both univariate and multivariate forecasting 🌟 scenarios.

Cheers!! 🍻

Contents 📄🗒

• Datasets Info📋

• Introduction to Time Series Analysis(Theory)🕰

- Taxonomy of Time Series Analysis Domain - Best Practices for Forecasting Model Selection - Simple and Classical Forecasting Methods - Time Series to Supervised Learning Problem - Deep Learning for Time Series Forecasting

• Time Series Data Visualization📉

- Plotting of Pandas Df - Adding title - Adding Axis label - X limits by slice - X limit by argument - Color and Style - X ticks spacing - Date formatting - Major and Minor axis values - Gridlines

• Time Series EDA📊

- Introduction with time series data - Time resampling - Time downsampling/upsampling - Time Shifting - forward shift - backward shift - Rolling window mean - Expanding window mean/cumulative mean

• Time Series Data Analysis💹

- Introduction to statsmodels - Hodrick Prescott filter - Trend/cyclical components - Time Series Stationarity - Augmented Dickey-Fuller Test - Granger Causality Tests - Time series decomposition - Additive/multiplicative models - Moving Average - Simple Exponentially weighted moving average(EWMA) - Double EWMA - Holt-Winters Method(Triple EWMA)

• Time Series Forecasting Classical Methods🤖

- Forecasting with Holts-Winter Method - Autocorrelation function(ACF) - Partial autocorrelation function(PACF) - Autocovariance for 1D - Autocorrelation for 1D - Autoregressive model(AR(p)) - Autoregressive Moving Average(ARMA) Model - Autoregressive Integrated Moving Average(ARIMA) - Error/Trend/Seasonal Decomposition(ETS Decomposition) - Seasonal Autoregressive Integrated Moving Averages(SARIMA) - Seasonal AutoRegressive Integrated Moving Average with EXogenous Variable.

• Time Series Forecasting with Deep Learning🕸️

- MLPs for time series forecasting - LSTMs for time series forecasting - CNNs for time series forecasting - Transformers for time series forecasting(under construction)

• Time Series Forecasting with FBProphet🎯

- Univariate and Multivariate Time Series Forecasting With FBProphet

• AutoML For Time Series Forecasting🦾

- Automating Time Series Forecsting with FLAML