Probabilistic Forecasting Reads

Paper - Master's Thesis : Comparison of probabilistic forecasting deep learning models in the context of renewable energy production

• DeepAR
• Wavenet
• Transformer
• Temporal Fusion Transformer
• Prophet

Awesome Reads

Timeseries ML

Code - Link

• Naive forecasting models (Naive, Seasonal Naive, Moving Average, etc)
• MXNet [10], developed by Amazon Web Services
• GluonTS has been developed by a Amazon Web Service team to fill the gap of time series modeling toolkit
• MQCNN, MQRNN, NBEATS and Wavenet does not outputs samples of a distribution function, but quantiles of the distribution itself
• NPTS is the implementation of the “Non-Parametric Time Series Forecaster” model
• MQCNN is the implementation of one variant of the model described in paper ”A Multi-Horizon Quantile Recurrent Forecaster”
• The model Transformer is the implementation of “Transformer” model architecture, as it was defined in paper [22]. It is described in this paper as ”The first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention”
• The model Wavenet is the implementation of ”Wavenet” model architecture, as it was defined in paper [23], with a quantized target. This model network is composed of dilated causal convolutional layers. Both residual and parameterised skip connections are used throughout the network,to speed up convergence and enable training of much deeper models
• DeepAR - global model from historical data of all time series. Similar to LSTM-based recurrent neural network architecture to the probabilistic forecasting problem
• Binomial distribution - Two possible outcomes (the prefix “bi” means two, or twice)
• Assumptions - Each trial is independent. The probability of success (tails, heads, fail or pass) is exactly the same for each trial
• Poisson distribution - Gives us the probability of a given number of events happening in a fixed interval of time
• Continuous distribution - data can take on any value within a specified range
• Discrete distribution is one in which the data can only take on certain values, for example integers
• RNN architecture for probabilistic forecasting, incorporating a negative Binomial likelihood
• Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. 

Spacetimeformer Multivariate Forecasting

Multivariate Time Series Forecasting with Transformers

Top 5 Forecasting Demos

Demand forecasting using RNN with LSTM on PyTorch

Anomaly Detection with PyOD

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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