Why marketing mix modeling is still hard to get right

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...
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 ...
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 ...
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 ...
Scientific Research Publishing

Supply Chain Demand Forecast Based on SSA-XGBoost Model ()

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts ...