In this article, we will see how transfer learning can be applied to time series forecasting, and how forecasting models can be trained once on a diverse time series dataset and used later on to obtain forecasts on different datasets without training. We will use the open-source Darts library to do all this with in a few lines of code. A self-contained notebook containing everything needed to reproduce the results is available here.
Time series forecasting has numerous applications in supply chain, energy, agriculture, control, IT operations, finance and other domains. For a long time, the best-performing approaches were relatively sophisticated statistical methods such as Exponential Smoothing or ARIMA. However, since recently, machine learning and deep learning have started to outperform these classical approaches on a number of forecasting tasks and competitions.
One of the distinctive features of machine learning models is that their parameters can be estimated on a potentially large number of series; unlike classical methods, which are usually estimated on a single series at a time. Although machine learning shows great potential, its utilisation still poses a few practical challenges. Read More
Daily Archives: June 16, 2022
Sponge Examples: Energy-Latency Attacks on Neural Networks
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While such devices enable us to train large-scale neural networks in datacenters and deploy them on edge devices, their designers’ focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully-crafted sponge examples, which are inputs designed to maximise energy consumption and latency, to drive machine learning (ML) systems towards their worst-case performance. Sponge examples are, to our knowledge, the first denial-of-service attack against the ML components of such systems. We mount two variants of our sponge attack on a wide range of state-of-the-art neural network models, and find that language models are surprisingly vulnerable. Sponge examples frequently increase both latency and energy consumption of these models by a factor of 30×. Extensive experiments show that our new attack is effective across different hardware platforms (CPU, GPU and an ASIC simulator) on a wide range of different language tasks. On vision tasks, we show that sponge examples can be produced and a latency degradation observed, but the effect is less pronounced. To demonstrate the effectiveness of sponge examples in the real world, we mount an attack against Microsoft Azure’s translator and show an increase of response time from 1ms to 6s (6000×). We conclude by proposing a defense strategy: shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective. Read More
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