Time Series Forecasting
LSTM for Time Series Forecasting
Univariate LSTM Models : one observation time-series data, predict the next value in the sequence
Multivariate LSTM Models : two or more observation time-series data, predict the next value in the sequence
Multiple Input Series : two or more parallel input time series and an output time series that is dependent on the input time series
Multiple Parallel Series : multiple parallel time series and a value must be predicted for each
Univariate Multi-Step LSTM Models : one observation time-series data, predict the multi step value in the sequence prediction.
Multivariate Multi-Step LSTM Models : two or more observation time-series data, predict the multi step value in the sequence prediction.
Multiple Input Multi-Step Output.
Multiple Parallel Input and Multi-Step Output.
Machine Learning for Multivariate Input
dafrie/lstm-load-forecasting: Electricity load forecasting with LSTM (Recurrent Neural Network) Dataset : Electricity Load ENTSO, Model : LSTM, Type: Multivariate
AIStream-Peelout/flow-forecast: Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting)., Dataset: river flow FlowDB Dataset - Flow Forecast - Flow Forecast, flood severity, Model: LSTM, Transformer, Simple Multi-Head Attention, Transformer with a linear decoder, DA-RNN, Transformer XL, Informer, DeepAR
Statistical Method for Multivariate Input
Machine Learning for Univariate Input
rishikksh20/LSTM-Time-Series-Analysis: Using LSTM network for time series forecasting Dataset: Sunspot Zurich, Model: LSTM
sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM: Time Series Forecasting using LSTM in Keras. Dataset: Air Pollution, Model: LSTM
pushpendughosh/Stock-market-forecasting: Forecasting directional movements of stock prices for intraday trading using LSTM and random forest Dataset: Stock Market, Model: LSTM, RF
demmojo/lstm-electric-load-forecast: Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network Dataset: Electric Consumption Model: LSTM
Yongyao/enso-forcasting: Improving the forecasting accuracy of ENSO through deep learning Dataset: ENSO El Nino, Model: LSTM
CynthiaKoopman/Forecasting-Solar-Energy: Forecasting Solar Power: Analysis of using a LSTM Neural Network Dataset: Solar power, Model: LSTM
3springs/attentive-neural-processes: implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout) Dataset: English power consumption, Model: ANP-RNN "Recurrent Attentive Neural Process for Sequential Data", ANP: Attentive Neural Processes, NP: Neural Processes, LSTM
Housiadas/forecasting-energy-consumption-LSTM: Development of a machine learning application for IoT platform to predict electric energy consumption in smart building environment in real time., Dataset: Kaggle energy consuption, Model: LSTM, Seq2Seq
Statistical Method for Univariate Input
Time Series Forecasting—ARIMA, LSTM, Prophet with Python - by Caner Dabakoglu - Medium : LSTM, ARIMA, Prophet
pyaf/load_forecasting: Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models Dataset: Electricity, Model: Feed forward Neural Network FFNN, Simple Moving Average SMA, Weighted Moving Average WMA, Simple Exponential Smoothing SES, Holts Winters HW, Autoregressive Integrated Moving Average ARIMA, Recurrent Neural Networks RNN, Long Short Term Memory cells LSTM, Gated Recurrent Unit cells GRU, Type: Univariate
jiegzhan/time-series-forecasting-rnn-tensorflow: Time series forecasting Dataset: Daily Temperature, Model: LSTM
zhangxu0307/time_series_forecasting_pytorch: time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code Dataset: Pollution, Solar Energy, Traffic data etec. Model MLP,RNN,LSTM,GRU, ARIMA, SVR, RF and TSR-RNN
rakshita95/DeepLearning-time-series: LSTM for time series forecasting Dataset: ?? Model: ARIMA, VAR, LSTM
mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM Dataset: Olypic, LeBron, Zika, Model: ARIMA dan LSTM
stxupengyu/load-forecasting-algorithms: 使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vector machine, BP neural network, GRU, LSTM) are used for power system load forecasting / power forecasting. Dataset: Power usage, Model: linear regression, random forest, support vector machine, BP neural network, GRU, LSTM
Abhishekmamidi123/Time-Series-Forecasting: Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons. Dataset: precipitation, Model: ARIMA, LSTM, FNN(Feed forward Neural Networks), TLNN(Time lagged Neural Networks), SANN(Seasonal Artificial Neural Networks
Jupyter Notebook Examples
Univariate ARIMA
import statsmodels
Univariate LSTM
import keras
Multivariate VAR
(Note: VAR should only for Stationary process - Wikipedia)
Multivariate LSTM
Prophet and Kats from Facebook
Note on Multivariate and Univariate
Software
Other Time Series
Kats - Kats One stop shop for time series analysis in Python
Precipitation Forecasting
Nowcasting, Upsampling, Interpolation, Super resolution
Deep Learning for Forecasting
top open source deep learning for time series forecasting frameworks.
Gluon This framework by Amazon remains one of the top DL based time series forecasting frameworks on GitHub. However, there are some down sides including lock-in to MXNet (a rather obscure architecture). The repository also doesn’t seem to be quick at adding new research.
Flow Forecast This is an upcoming PyTorch based deep learning for time series forecasting framework. The repository features a lot of recent models out of research conferences along with an easy to use deployment API. The repository is one of the few repos to have new models, coverage tests, and interpretability metrics.
sktime dl This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.
PyTorch-TS Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn’t that active (last commit was in November).
eBook Forecasting
NIST/SEMATECH e-Handbook of Statistical Methods engineering statistics
Timeseries Forecasting
Timeseries Forecasting Book
Timeseries Forecasting Reading
Timeseries RNN
Timeseries Forecasting
Time-series Forecasting
VAR
time Series
LSTM
Time Series Toolbox
Prophet Prophet | Forecasting at scale.
XGboost, LGBM, pmdarima, stanpy (for bayesian modelling)
Prophet - seems to be the current 'standard' choice
ARIMA - Classical choice
Exponential Moving Average - dead simple to implement, works well for stuff that's a time series but not very seasonal
Kalman/Statespace model - used by Splunk's predict[1] command (pretty sure I always used LLP5)
Prophet, statsmodels, tf.keras for RNNs.
tensorflow probability's time series package.
PyTorch for recurrent nets
State of the art is 1D convnets, bleeding edge is transformers.
pycaret timeseries
lgbm light gbm
cvxpy
TensorFlow's LSTMCell
LSTMs have been going the way of the dinosaurs since 2018. If you really need a complex neural network (over 1D convolution approaches), transformers are the current SOTA. Demand forecasting with the Temporal Fusion Transformer—pytorch-forecasting documentation
sktime
bssts
statsmodels
Books
Forecasting Comparison
statsforecast/experiments/m3 at main · Nixtla/statsforecast # Statistical vs Deep Learning forecasting methods
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