Alternatives and similar repositories for kaggle-m5-forecasting-accuracy-4th-place

Users that are interested in kaggle-m5-forecasting-accuracy-4th-place are comparing it to the libraries listed below

• matthiasanderer / m5-accuracy-competition
  My second place solution in the M5 Accuracy competition
  ☆70Updated 4 years ago
• IoannisNasios / M5_Uncertainty_3rd_place
  M5 Uncertainty kaggle competition, 3rd place solution
  ☆39Updated last year
• apalle1 / M5-Hierarchical-Time-Series-Forecasting
  LGBM
  ☆16Updated 4 years ago
• KunalArora / kaggle-m5-forecasting
  Time-Series forecasting using Stats models, LightGBM & LSTM
  ☆38Updated 5 years ago
• DeepWisdom / AutoSeries2019
  ☆18Updated 5 years ago
• marisakamozz / m5
  Kaggle M5 Forecasting - Uncertainty 4th Place Solution
  ☆32Updated 2 years ago
• btrotta / kaggle-m5
  Top 3% solution for Kaggle M5 Accuracy competition
  ☆23Updated 5 years ago
• bits-bytes-nn / mofc-demand-forecast
  Time Series Forecasting for the M5 Competition
  ☆41Updated 3 years ago
• manujosephv / deeprenewalprocess
  GluonTS Implementation of Intermittent Demand Forecasting with Deep Renewal Processes arXiv:1911.10416v1 [cs.LG]
  ☆31Updated 3 years ago
• DeepBlueAI / AutoSeries
  ☆15Updated 5 years ago
• elephaint / hfas
  Hierarchical Forecasting at Scale
  ☆14Updated last year
• lovvge / Forecasting-Tutorials
  ☆23Updated 3 years ago
• EvgeniyaMartynova / MLiP_M5
  ☆25Updated 5 years ago
• HumaticsLAB / GTM-Transformer
  Official Implementation of paper: Well Googled is Half Done: Multimodal Forecasting of New FashionProduct Sales with Image-based Google T…
  ☆53Updated 3 years ago
• Trigram19 / m5-python-starter
  Python framework for a good neural network for the Makidrakis 5 (M5) competition hosted on Kaggle.
  ☆22Updated 5 years ago
• saritmaitra / Segmentation-Clustering
  Recency, Frequency, and Monetary are three behavioral attributes and are quite simple, in that they can be easily computed for any databa…
  ☆15Updated last year
• Valdecy / pyInterDemand
  A python Library for Intermittent Demand Methods: Croston, SBA, SBJ, TSB, HES, LES and SES
  ☆36Updated last year
• Qiuyan918 / m5_forcasting
  https://www.kaggle.com/c/m5-forecasting-accuracy/overview/
  ☆12Updated 4 years ago
• Willofd101 / ziln_cltv
  A python package to train & evaluate Customer Lifetime Value(CLTV) models using Neural Networks & ZILN loss(developed by google)
  ☆30Updated 2 years ago
• fehiepsi / web-traffic-time-series-forecasting
  kaggle competition: https://www.kaggle.com/c/web-traffic-time-series-forecasting
  ☆16Updated 7 years ago
• PGuti / Uplift
  About uplift modeling
  ☆30Updated 8 years ago
• jingmin1987 / variable-clustering
  A re-creation of SAS varclus procedure in Python
  ☆23Updated 6 years ago
• rk2900 / deep-conv-attr
  An implementation of our CIKM 2018 paper "Deep Conversion Attribution with Dual-attention Recurrent Neural Network"
  ☆62Updated 6 years ago
• antklen / kaggle_favorita-grocery-sales
  12th place solution for Kaggle Corporación Favorita Grocery Sales Forecasting
  ☆15Updated 7 years ago
• pourya-ir / Medium
  ☆49Updated 5 years ago
• tblume1992 / LazyProphet
  Time Series Forecasting with LightGBM
  ☆85Updated 2 years ago
• carlomazzaferro / scikit-hts-examples
  Example usage of scikit-hts
  ☆57Updated 3 years ago
• jwmueller / BanditDynamicPricing
  Bandit algorithms for dynamic pricing of many products
  ☆42Updated 5 years ago
• baosenguo / Kaggle-MoA-2nd-Place-Solution
  ☆168Updated 4 years ago
• brian-c-ogorman / ABanditTesting
  No Regrets: A deep dive comparison of bandits and A/B testing
  ☆47Updated 7 years ago