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

Conditional time series forecasting with convolutional neural networks

Forecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these dependencies by a convolutional neural network. In particular the focus is on multivariate time series forecasting. Effectively, we use multiple financial time series as input in the neural network, thus conditioning the forecast of a time series x(t) on both its own history as well as that of a second (or third) time series y(t). Training a model on multiple stock series allows the network to exploit the correlation structure between these series so that the network can learn the market dynamics in shorter sequences of data. We show that long-term temporal dependencies in and between financial time series can be learned by means of a deep convolutional neural network based on the WaveNet model [2]. The network makes use of dilated convolutions applied to multiple time series so that the receptive field of the network is wide enough to learn both short and long-term dependencies. The architecture includes batch normalization and uses a 1 × k convolution with parametrized skip connections from the input time series as well as the time series we condition on

Forecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach

A decline in the number of construction engineers and inspectors available at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in this sector. One of the crucial aspects of workforce planning involves forecasting the required workforce for any industry or agency. This thesis developed machine learning models to estimate the person-hour requirements of STAs at the agency and project levels. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee data between 2012 and 2021. At the project level, machine learning regressors ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. At the agency level, a classic time series modeling approach, as well as neural networks-based models, were developed to forecast the monthly person-hour requirements of the agency. Parametric and non-parametric tests were employed in comparing the models across both levels. The results indicated a high performance from the random forest regressor, a tree ensemble with bagging, which recorded an average R-squared value of 0.91. The one-dimensional convolutional neural network model was the most effective model for forecasting the monthly person requirements at the agency level. It recorded an average RMSE of 4,500 person-hours monthly over short-range forecasting and an average of 5,000 person-hours monthly over long-range forecasting. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management

Towards Better Forecasting by Fusing Near and Distant Future Visions

Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance. Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy. To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by the Construal Level Theory of psychology, this model aims to improve the predictive performance by fusing forecasting information (i.e., future visions) of different future time. We first use the Convolution Neural Network to extract multi-level abstract representations of the raw data for near and distant future predictions. We then model the interplay between multiple predictive tasks and fuse their future visions through a modified Encoder-Decoder architecture. Finally, we combine traditional Autoregression model with the neural network to solve the scale insensitive problem. Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric.Comment: Accepted by AAAI 202

Forecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach

A decline in the number of construction engineers and inspectors available at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in this sector. One of the crucial aspects of workforce planning involves forecasting the required workforce for any industry or agency. This thesis developed machine learning models to estimate the person-hour requirements of STAs at the agency and project levels. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee data between 2012 and 2021. At the project level, machine learning regressors ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. At the agency level, a classic time series modeling approach, as well as neural networks-based models, were developed to forecast the monthly person-hour requirements of the agency. Parametric and non-parametric tests were employed in comparing the models across both levels. The results indicated a high performance from the random forest regressor, a tree ensemble with bagging, which recorded an average R-squared value of 0.91. The one-dimensional convolutional neural network model was the most effective model for forecasting the monthly person requirements at the agency level. It recorded an average RMSE of 4,500 person-hours monthly over short-range forecasting and an average of 5,000 person-hours monthly over long-range forecasting. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.Comment: accepted to be published in Intelligent Automation and Soft Computing journa

Machine Learning based Models for Fresh Produce Yield and Price Forecasting for Strawberry Fruit

Building market price forecasting models of Fresh Produce (FP) is crucial to protect retailers and consumers from highly priced FP. However, the task of forecasting FP prices is highly complex due to the very short shelf life of FP, inability to store for long term and external factors like weather and climate change. This forecasting problem has been traditionally modelled as a time series problem. Models for grain yield forecasting and other non-agricultural prices forecasting are common. However, forecasting of FP prices is recent and has not been fully explored. In this thesis, the forecasting models built to fill this void are solely machine learning based which is also a novelty. The growth and success of deep learning, a type of machine learning algorithm, has largely been attributed to the availability of big data and high end computational power. In this thesis, work is done on building several machine learning models (both conventional and deep learning based) to predict future yield and prices of FP (price forecast of strawberries are said to be more difficult than other FP and hence is used here as the main product). The data used in building these prediction models comprises of California weather data, California strawberry yield, California strawberry farm-gate prices and a retailer purchase price data. A comparison of the various prediction models is done based on a new aggregated error measure (AGM) proposed in this thesis which combines mean absolute error, mean squared error and R^2 coefficient of determination. The best two models are found to be an Attention CNN-LSTM (AC-LSTM) and an Attention ConvLSTM (ACV-LSTM). Different stacking ensemble techniques such as voting regressor and stacking with Support vector Regression (SVR) are then utilized to come up with the best prediction. The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-LSTM using a linear SVR is the best performing based on the proposed aggregated error measure. To show the robustness of the proposed model, it was used also tested for predicting WTI and Brent crude oil prices and the results proved consistent with that of the FP price prediction