Time-Series Analysis and Forecasting with Python 📈📉

Welcome to a comprehensive guide on Time-Series Analysis, Forecasting, and Machine Learning using Python.

This repository is designed to take you from a foundational understanding to advanced practice. Whether you are dealing with classical forecasting (ARIMA, Prophet), framing time series as supervised machine learning (XGBoost, LightGBM), diving into state-of-the-art Deep Learning (LSTMs, CNNs, Transformers), or tackling advanced industrial use-cases like Anomaly Detection, Clustering, and Classification—this repository provides practical, code-first implementations.

The contents are structured logically: starting with foundational Exploratory Data Analysis (EDA) and statistical analysis, moving through classical methodologies, and transitioning into cutting-edge machine learning and AutoML frameworks.

💡 Quick Model Comparison Guide

Not sure which model to use for your forecasting task? Here is a high-level comparison of the approaches covered in this repository to help you decide:

| Approach / Model | Complexity | Best Suited For | Pros | Cons | | :--- | :---: | :--- | :--- | :--- | | Statistical (ARIMA/SARIMAX) | Low | Small datasets, clear trends/seasonalities. | Highly interpretable, strong mathematical foundation. | Fails on complex non-linear patterns; requires strict stationarity. | | Tree-Based ML (XGBoost/LGBM) | Medium | Tabularized time series with external features. | Fast, highly accurate, handles non-linearities well. | Requires manual feature engineering (lags, rolling stats). | | Deep Learning (LSTMs/CNNs) | High | Large datasets, complex sequential patterns. | Learns temporal dependencies automatically. | Computationally expensive; "black box" nature. | | Transformers | Very High | Massive datasets, long-term forecasting. | State-of-the-art accuracy; captures long-range context. | Extremely data-hungry; prone to overfitting on small data. | | FBProphet | Low | Business time series (holidays, daily/weekly seasonality). | Works out-of-the-box, handles missing data gracefully. | Less flexible for highly irregular or high-frequency data. | | AutoML (FLAML) | Low | Robust baselines without manual tuning. | Automatically finds the best algorithm and hyperparameters. | Computationally heavy during the search phase. |

📑 Contents

1. 🏗️ Fundamentals and Analysis

Build a strong foundation in time-series concepts, visualization, and statistical analysis.

• 📋 Datasets Info
• 📚 Introduction to Time Series Analysis (Theory)
• 📊 Time Series Data Visualization
• 🔬 Time Series EDA
• 📉 Time Series Data Analysis

2. 🔮 Forecasting & Machine Learning

Forecast future values using both classical statistical techniques and modern machine learning.

• 📈 Time Series Forecasting Classical Methods
• ⚙️ Time Series Feature Engineering & Machine Learning
• 🎯 Time Series Forecasting with FBProphet
• 🦾 AutoML For Time Series Forecasting (FLAML)

3. 🧠 Deep Learning

Leverage neural networks to capture complex, non-linear relationships in sequential data.

• 🕸️ MLPs for Time Series Forecasting
• 🔁 LSTMs for Time Series Forecasting
• 🖼️ CNNs for Time Series Forecasting
• 🤖 Transformers for Time Series Forecasting

4. 🚀 Advanced Time Series Tasks

Go beyond forecasting to tackle real-world industrial problems.

• 🗂️ Time Series Classification
• 🚨 Time Series Anomaly Detection
• 🧩 Time Series Clustering

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Created and maintained by Ajit Kumar Singh.