GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
GitHub

Step-by-Step ARIMA Model Building

The study explores the effectiveness of the ARIMA(3,1,3) model in predicting market trends, specifically accounting for macroeconomic shifts like the 2026 CPI base year updates. Stationarity ...
Scientific Research Publishing

A Comparative Analysis of the Performance of Machine Learning and Deep Learning Techniques in Predicting Stock Prices ()

1 Information System Department, Faculty of Commerce and Business Administration Helwan University, Cairo, Egypt. 2 Computer Science Department, Faculty of Computer and Artificial Intelligence, Helwan ...
Frontiers

Time series forecasting-based Kubernetes autoscaling using Facebook Prophet and Long Short-Term Memory

The advancement of cloud computing technologies has led to increased usage in application deployment in recent years. Kubernetes, a widely used container orchestration platform for deploying ...
Frontiers

Data pipeline for real-time energy consumption data management and prediction

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning ...
Analytics India Magazine

Quick way to find p, d and q values for ARIMA

To make a better explanation of ARIMA we can also write it as (AR, I, MA) and by this, we can assume that in the ARIMA, p is AR, d is I and q is MA. ARIMA models integrate Auto Regression, Moving ...