SoloQuant Flagship Program Early Bird · $420 USD (Save $379) · Sep 1–Dec 1, 2026 · Launch Jan 1, 2027 →

SoloQuant Academy

Short evening lessons for people who work. Quant trading, Python, data, systems, architecture.

Information Hub Student Portal Privacy Policy Terms & Conditions

Academy notes

Information Hub

Articles and updates from SoloQuant Academy.

Tag: paper

pythonpapertime-seriestftforecastingafter-work

Introducing Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting for after-work quants

Lim, Arik, Loeff and Pfister (IJF 2021; arXiv:1912.09363) build TFT for multi-horizon forecasts that mix static covariates, past-observed series, and known-future inputs (calendar, promotions). GRNs + GLU skip unused parts; variable selection names which features matter; LSTM does local processing; interpretable multi-head attention does long range; quantiles 0.1/0.5/0.9. Electricity, Traffic, Favorita, volatility. Plain-language intro. Not investment advice.

pythonpapertime-seriesnhitsforecastingafter-work

Introducing N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting for after-work quants

Challu, Olivares, Oreshkin, Garza, Mergenthaler-Canseco and Dubrawski (AAAI 2023; arXiv:2201.12886) keep N-BEATS residual MLP stacks, then add MaxPool multi-rate sampling and hierarchical interpolation so each block owns a frequency band. Univariate, shared weights. About 16% MSE / 14% MAE vs Transformer LTSF baselines, about 50 times faster. NeuralForecast. Plain-language intro. Not investment advice.

pythonpapertime-seriesmomentfoundation-modelafter-work

Introducing MOMENT: A Family of Open Time-series Foundation Models for after-work quants

Goswami, Szafer, Choudhry, Cai, Li and Dubrawski (CMU Auton Lab, ICML 2024; arXiv:2402.03885) release open TS foundation models (40M / 125M / 385M). Masked patch reconstruction on the Time Series Pile; one encoder for forecast, classify, impute, anomaly. Hugging Face AutonLab/MOMENT-1-large. Plain-language intro. Not investment advice.

pythonpapertime-seriesnbeatsforecastingafter-work

Introducing N-BEATS: Neural basis expansion analysis for interpretable time series forecasting for after-work quants

Oreshkin, Carpov, Chapados and Bengio (ICLR 2020; arXiv:1905.10437) stack fully-connected residual blocks with a forecast/backcast fork. No TS feature engineering. Generic N-BEATS beats the M4 hybrid winner by ~3% OWA; an interpretable trend+Fourier variant stays within a hair. M3 and tourism too. Plain-language intro. Not investment advice.

pythonpapertime-seriesinformertransformerafter-work

Introducing Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting for after-work quants

Zhou, Zhang, Peng, Zhang, Li, Xiong and Zhang (AAAI 2021; arXiv:2012.07436) fix three Transformer LSTF bottlenecks: quadratic attention (ProbSparse O(L log L)), stacked-layer memory (self-attention distilling), and slow step-by-step decode (one-shot generative decoder). ETT oil temperature, ECL, Weather. Plain-language intro. Not investment advice.

pythonpapertime-seriesmomentfoundation-modelafter-work

Introducing MOMENT: A Family of Open Time-series Foundation Models for after-work quants

pythonpapertime-seriesfedformertransformerafter-work

Introducing FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting for after-work quants

Zhou, Ma, Wen, Wang, Sun and Jin (Alibaba, ICML 2022; arXiv:2201.12740) mix seasonal-trend decomposition with frequency-domain Transformer blocks. Randomly keep a fixed number of Fourier (or Wavelet) modes so attention is linear in length. vs Autoformer: about 14.8% / 22.6% lower MSE on multivariate / univariate LTSF. Plain-language intro. Not investment advice.

pythonpapertime-seriesinformertransformerafter-work

Introducing Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting for after-work quants

pythonpapertime-seriesnbeatsforecastingafter-work

Introducing N-BEATS: Neural basis expansion analysis for interpretable time series forecasting for after-work quants

pythonpapertime-seriesfedformertransformerafter-work

Introducing FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting for after-work quants

pythonpapertime-seriesmomentfoundation-modelafter-work

Introducing MOMENT: A Family of Open Time-series Foundation Models for after-work quants

pythonpapertime-serieslstmattentionafter-work

Introducing EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction for after-work quants

Li, Zhu, Kong, Han and Zhao (2018; arXiv:1811.03760) put an evolutionary attention layer in front of an LSTM. Attention weights over the look-back window are evolved with competitive random search (genetic-style select/recombine/mutate), not backprop, to avoid local traps. Beijing PM2.5, SML2010, MSR Action3D. Plain-language intro. Not investment advice.

← Previous

Page 2 of 13