time-series-analysis-and-forecasting

Keeping Inventory of spare in various service centre to the market demand is always a challenge as most service centres spends significant amount in spare parts inventory costs. In spite of this, availability of spare parts is been one of the problem areas.

Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting

A two-part project focused on feature engineering on csv data

High-recall solar flare early warning system using GOES X-ray data, trained on PARAM Utkarsh HPC and deployed with Streamlit.

In this section, we will use machine learning algorithms to perform time series analysis.

Hybrid Ridge + LightGBM pipeline over 95k+ half-hourly demand records. Engineered Fourier seasonality, lag, and rolling-window features.

Predictive data analysis project using air quality, weather, traffic, and respiratory data to compare machine learning and deep learning models.

Dynamic Mode Decomposition for EEG signal analysis - Extract spatio-temporal coherent patterns from neural recordings

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Prediction of road casualties and evaluate the impact of transformations in Time Series Modeling and Forecasting with ARIMA using the R programming language

Time Series Analysis and Stock Price Prediction of Reliance Industries using ARIMA (1, 1, 0) in Python.

This repository demonstrates an end-to-end CTR time-series forecasting pipeline using SARIMA model. It analyzes daily advertising performance to uncover trend, weekly seasonality, and engagement patterns, and delivers 30-day forecasts to support budgeting, pacing, and marketing decision-making.

Teaching Lab Notes of "Time Series Analysis"@NTUsg by Patrick PUN Chi Seng

End-to-end financial analytics architecture integrating IFRS financial modeling, a PostgreSQL-based financial data warehouse, Python forecasting models, and Power BI executive dashboards.

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Time series decomposition and forecasting of CALABARZON elementary school enrollment (2010–2020) using ARIMA and Prophet, with ADF stationarity testing and model evaluation via MAE/RMSE/MAPE.

Internet Traffic Prediction Using Time Series Forecasting Models SARIMA and LSTMModels

Nerding out to understand stationarity, validate time series models, compare ARIMA vs ML, avoid data leakage

An interactive Streamlit dashboard for Time Series Analysis (TSA) and forecasting, originally developed for a project at IIT Guwahati.

This project explores retail chain performance in New Zealand by combining sales forecasting, regional sales analysis, and supply chain insights. Using real data and Python (including Prophet for forecasting), it uncovers patterns and helps identify areas for improvement in inventory and operations.