相关论文: Machine learning modeling for time series problem:…
Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial…
In this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination. While…
Airline operations are subject to many uncertainties, such as weather, varying demand, maintenance events, congestion, etc. Large amounts of information are currently ignored due to difficulties in processing big data sets. We explore the…
In this study, machine learning models were tested to predict whether or not a customer of an insurance company would purchase a travel insurance product. For this purpose, secondary data provided by an open-source website that compiles…
We consider transportation networks that are modeled by dynamic graphs, and introduce the possibility for traveling agents to use Backward Time-Travel (BTT) devices at any node to go back in time (to some extent, and with some appropriate…
Machine Learning focuses on the construction and study of systems that can learn from data. This is connected with the classification problem, which usually is what Machine Learning algorithms are designed to solve. When a machine learning…
Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation…
This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various…
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage.…
Analyzing better time series with limited human effort is of interest to academia and industry. Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present…
The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices…
Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non-stationarity. This phenomenon is pronounced in the South Australian region of the…
In this paper, we propose machine learning solutions to predict the time of future trips and the possible distance the vehicle will travel. For this prediction task, we develop and investigate four methods. In the first method, we use long…
It becomes a significant challenge to predict customer behavior and retain an existing customer with the rapid growth of digitization which opens up more opportunities for customers to choose from subscription-based products and services…
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, these were shown to systematically present a lower…
Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the…
This study investigates the performance of machine learning models in forecasting electricity Day-Ahead Market (DAM) prices using short historical training windows, with a focus on detecting seasonal trends and price spikes. We evaluate…
The two somewhat conflicting requirements of efficiency and fairness make ATFM an unsatisfactorily solved problem, despite its overwhelming importance. In this paper, we present an economics motivated solution that is based on the notion of…
Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and uncertainty about future outcomes, and then make a decision. In this paper, we propose a new…
The planning of attractive and cost efficient public transport systems is a highly complex optimization process involving many steps. Integrating robustness from a passenger's point of view makes the task even more challenging. With…