328 research outputs found
Multiple Time Series Forecasting with Temporal Fusion Transformers
The goal of this thesis is to present the Temporal Fusion Transformer model and to evaluate its forecasting capabilities across multiple time series. Its contribution to the field of multi-horizon, multiple time series forecasting is explored, with great focus on the interpretability feature offered by the model.
It is observed how improvements to the model performances can be achieved when paired with a form of clustering on the target entities, either by exploiting the natural categorization of the time series considered or by associating similar entities by means of a clustering algorithm on the target variable
Professor Text: University Fundraising Optimization
University fundraising campaigns are a unique type of cause-related marketing with its own challenges and opportunities. Campaigns like this typically last an extended period, such as five or more years, and goals exist beyond the dollar amount raised. These supplemental goals, such as awareness among potential future donators or brand reputation within the local community, are important to consider and strategize. There can also be unique limitations, such as requiring advertising specifically on recent large gifts or endowment programs. This research explores how machine learning techniques such as natural language processing can be used to optimize a fundraising campaign strategy, execution, and overall performance
Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting
Forecasting how landslides will evolve over time or whether they will fail is
a challenging task due to a variety of factors, both internal and external.
Despite their considerable potential to address these challenges, deep learning
techniques lack interpretability, undermining the credibility of the forecasts
they produce. The recent development of transformer-based deep learning offers
untapped possibilities for forecasting landslides with unprecedented
interpretability and nonlinear feature learning capabilities. Here, we present
a deep learning pipeline that is capable of predicting landslide behavior
holistically, which employs a transformer-based network called LFIT to learn
complex nonlinear relationships from prior knowledge and multiple source data,
identifying the most relevant variables, and demonstrating a comprehensive
understanding of landslide evolution and temporal patterns. By integrating
prior knowledge, we provide improvement in holistic landslide forecasting,
enabling us to capture diverse responses to various influencing factors in
different local landslide areas. Using deformation observations as proxies for
measuring the kinetics of landslides, we validate our approach by training
models to forecast reservoir landslides in the Three Gorges Reservoir and
creeping landslides on the Tibetan Plateau. When prior knowledge is
incorporated, we show that interpretable landslide forecasting effectively
identifies influential factors across various landslides. It further elucidates
how local areas respond to these factors, making landslide behavior and trends
more interpretable and predictable. The findings from this study will
contribute to understanding landslide behavior in a new way and make the
proposed approach applicable to other complex disasters influenced by internal
and external factors in the future
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic
operation of power grids and energy markets. However, the non-linearity and
non-stationarity of electricity demand as well as its dependency on various
external factors renders STLF a challenging task. To that end, several deep
learning models have been proposed in the literature for STLF, reporting
promising results. In order to evaluate the accuracy of said models in
day-ahead forecasting settings, in this paper we focus on the national net
aggregated STLF of Portugal and conduct a comparative study considering a set
of indicative, well-established deep autoregressive models, namely multi-layer
perceptrons (MLP), long short-term memory networks (LSTM), neural basis
expansion coefficient analysis (N-BEATS), temporal convolutional networks
(TCN), and temporal fusion transformers (TFT). Moreover, we identify factors
that significantly affect the demand and investigate their impact on the
accuracy of each model. Our results suggest that N-BEATS consistently
outperforms the rest of the examined models. MLP follows, providing further
evidence towards the use of feed-forward networks over relatively more
sophisticated architectures. Finally, certain calendar and weather features
like the hour of the day and the temperature are identified as key accuracy
drivers, providing insights regarding the forecasting approach that should be
used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble,
N-BEATS, Temporal Convolution, Forecasting Accurac
Online advertising revenue forecasting: an interpretable deep learning approach
This paper investigates whether publishers’ Google AdSense online advertising revenues can be predicted from peekd’s proprietary database using deep learning methodologies. Peekd is a Berlin (Germany) based data science company, which primarily provides e Retailers with sales and shopper intelligence. I find that using a single deep learning model, AdSense revenues can be predicted across publishers. Additionally, using unsupervised clustering, publishers were grouped and related time series were fed as covariates when making predictions. No performance improvement was found in relation with this technique. Finally, I find that in the short-term, publishers’ AdSense revenues embed similar temporal patterns as web traffic
MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting
Time series forecasting has received wide interest from existing research due
to its broad applications and inherent challenging. The research challenge lies
in identifying effective patterns in historical series and applying them to
future forecasting. Advanced models based on point-wise connected MLP and
Transformer architectures have strong fitting power, but their secondary
computational complexity limits practicality. Additionally, those structures
inherently disrupt the temporal order, reducing the information utilization and
making the forecasting process uninterpretable. To solve these problems, this
paper proposes a forecasting model, MPR-Net. It first adaptively decomposes
multi-scale historical series patterns using convolution operation, then
constructs a pattern extension forecasting method based on the prior knowledge
of pattern reproduction, and finally reconstructs future patterns into future
series using deconvolution operation. By leveraging the temporal dependencies
present in the time series, MPR-Net not only achieves linear time complexity,
but also makes the forecasting process interpretable. By carrying out
sufficient experiments on more than ten real data sets of both short and long
term forecasting tasks, MPR-Net achieves the state of the art forecasting
performance, as well as good generalization and robustness performance
Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series
Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios
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