Comparative Analysis of LSTM, ARIMA, and Facebook’s Prophet for Traffic Forecasting: Advancements, Challenges, and Limitations

Accurate short-term traffic forecasting plays a crucial role in Intelligent Transportation Systems for effective traffic management and planning. In this study, the performances of three popular forecasting models are explored: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Facebook's Prophet, for short-term...

Partial Hierarchy Appliance Modelling In Household Energy Consumption: Utilizing ARMA based methods to improve the prediction of household energy consumption

The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at...

Kiosk Strategic Demand Forecasting with Scenario Planning: A case study at SITA

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order...

Rescue of groundwater level time series: How to visually identify and treat errors”

Groundwater level time series are of great value for a variety of groundwater studies, particularly for those dealing with the impacts of anthropogenic and climate change. Quality control of groundwater level observations is an essential step prior to any further application, e.g., trend analysis. Often the quality control of data is limited to...

Forecasting tourist counts with historical counts and external features

This research explored two types of models, ARIMA and multiple linear regression, for forecasting tourist counts in 7 locations around Amsterdam red light district, for the prediction horizon of up to 30 minutes.

Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data...

Traffic optimization using a novel traffic controller featuring distributed agents and forecasting

Using some sort of adaptive traffic light control system is becoming standard policy among metropolitan areas. However, controlling traffic lights efficiently on a city-wide scale is computationally intensive and theoretically complex. This paper aims to show a proof of concept of an efficient and modular traffic. light controller with...

Energy Management System with PV Power Forecast to Optimally Charge EVs at the Workplace

This paper presents the design of an energy management system (EMS) capable of forecasting photovoltaic (PV) power production and optimizing power flows between PV system, grid, and battery electric vehicles (BEVs) at the workplace. The aim is to minimize charging cost while reducing energy demand from the grid by increasing PV self...

Volatility in Electrical Load Forecasting for Long-term Horizon: An ARIMA-GARCH Approach

Electrical load forecasting in long-term horizon of power systems plays an important role for system planning and development. Load forecast in long-term horizon is represented as time-series. Thus, it is important to check the effect of volatility in the forecasted load time-series. In short, volatility in long-term horizon affects four main...