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.

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

End-to-end data pipeline analyzing 41GB (15B views) of Wikimedia telemetry via out-of-core DuckDB. Quantifies system volatility, filters statistical noise, and deploys a serverless interactive dashboard.

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

A repository containing 8 alpha signals with a signal combination engine incorporate a risk model, with 76+ automated tests.

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

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

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

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

Internet Traffic Prediction Using Time Series Forecasting Models SARIMA and LSTMModels

определить характеристики и с их помощью спрогнозировать длительность поездки такси

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.

Analyzing Marketing Resilience During the Pandemic Era

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

This is the task-1 of let's grow more virtual internship program. The aim of the task is as a security/defense analyst, try to find out the hot zone of terrorism.

A comprehensive time series analysis project that forecasts monthly accidental deaths in the USA using statistical models. This project demonstrates exponential smoothing and ARIMA modeling techniques to predict future values based on historical data from January 1973 to December 1978.

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This repository contain projects completed during my graduate study in Data Science & Analytics at the J. Mack Robinson College of Business, Georgia State University. I worked as part of a team of 4 or 6 members and we equally contributed in completing tasks and preparing final documentations (code file, report & PowerPoint presentation).

Developed during my internship at Tata Motors, this interactive dashboard monitors and analyzes electrical power usage using Dash and Plotly. It offers shift-wise analysis, trend/seasonality detection, and a 7-day SARIMA consumption forecast. Users can filter data by stations, shifts, and custom date ranges for detailed insights.

This work centers on assessing and comparing predictive models for regression and time series prediction using specific datasets, with the goal of selecting the most effective methodology for unseen test data.

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.

This repository provides code implementations for four challenges from the Artificial Neural Networks and Deep Learning Challenges, covering both the [Edition 2023] and [Edition 2024]. It offers practical solutions and insights into various deep learning problems.

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