Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system

Forecasting Emergency Department Volumes Using Time Series and Other Techniques

The aim of this research is to forecast patient volumes in the Emergency Department of a regional hospital in Minnesota, which eventually will aid in addressing the issue of registered nurse staffing fluctuation, more specifically, productivity and capacity planning in the ED. Several methods are applied to forecast arrival patient volume, and cumulative patient volume to evaluate each model’s performance. The methods considered are linear regression, time series models and dynamic latent factor method. Long term forecast for as long as six months ahead is the goal here due union regulations that only allows for significant changes in registered nurse staffing schedule be put in place six months in advance. This long term forecast will enable administrators implement effective and timely changes to enhance productivity. The patient arrival count, where each patient is counted once in the system, is analyzed to see how many patients the department encounters hourly. Also, cumulative patient count which gives us an idea of how many patients are in the department at any given time was also considered, here patients are counted for every hour they are in the emergency department (ED). Patient who come to the ED are categorized by their acuity level. Of all the patients that came to the ED, 52% need urgent care; this group is also analyzed to predict their arrival volume. Lastly data was simulated with different patterns and the forecasting results from the different methods were compared and estimated. The forecast accuracy and performance for these models is then evaluated using out-of-sample forecasts for up to six months ahead. Mean square error (MSE), Root mean square error (RMSE) and mean absolute error (MAE) were utilized tosee which method is most reliable and also consistent