# TODO

## ~~save / reload the model~~
self.serialize() => to_dict every where + json

## ~~forecast future values => update datasets + forecast~~

## ~~signal transformation~~ almost done
- ~~cumulative (integrated) tranformation~~ OK

## ~~Cutting into Estim/Valid (NE*H , NV*H)~~  => cross-validation
1. ~~ratio_E = 0.5~~
2. ~~N_train = N_Estim + N_Valid + N_test (=H)~~
3. ~~N_Estim = ratio_E * (N_train - H) ~~

## ~~prediction intervals
1. ~~improve plotting ... shaded area around prediction intervals
   neeed prediction intervals first

## ~~use cross validation 
2. http://robjhyndman.com/hyndsight/tscvexample/
3. book : https://www.otexts.org/fpp
4. https://www.otexts.org/fpp/2/5


## Residual diagnostics	
	https://www.otexts.org/fpp/2/6

## other trends
1. Exponential smoothing      
2. ~~exogenous variables~~  OK.
3. ~~moving average(N)~~. OK
4. ~~moving median(N)~~. OK

## other cycles
	1. seasonal
	2. user holidays etc (external tables?)

## other AR
1. ~~ARX~~ OK.
2. VAR ?
2. order control (look at timedelta ??).

## Configuration (Options)
1.  ~~activate/disable transfromations/models/decomposition~~.  OK
2. ~~configure trends~~ OK.
3. ~~configure cycles (CycleLength = ?)~~. OK
	- cycle length should be in [5, 7, 12, 24 , 30, 60]
4. ~~configure ARs (p = ?)~~ OK.
5. ~~processing : threads etc~~ OK.
  

## Benchmarking
1. ~~MComp~~ OK.
2. ~~NN5~~ OK.
3. ~~NN3~~ OK.
4. ~~Yahoo stocks~~ OK.
	
## speedup things
1. python is sloooooooooooow (cython ?)
2. multiprocessing seems OK

## timedelta adaptive estimation
1. ~~allow user control~~. OK.
2. ~~truncate timedelta to the nearest unit.~~ OK.
3. avoid saturday/sunday if not present in the dataset.
  
## LOGGING

## ~~cross validation for time series
=> http://robjhyndman.com/talks/MelbourneRUG.pdf

## ~~feature selection (remove unnecessary lags and exogenous variables).
===> smaller model => smaller SQL code !!!


## ~~real-life examples :
http://stackoverflow.com/questions/10302261/forecasting-time-series-data ~~~~ OK

## better graphics
https://stanford.edu/~mwaskom/software/seaborn/

## ~~GitHub Topics
autoregressive benchmark cycles data-frame exogenous forecasting heroku hierarchical horizon  jupyter machine-learning-library pandas restful-api scikit-learn seasonal sql sql-generation time-series trends 

# Forecast Competition

http://eem2017.com/program/forecast-competition

In cooperation with our technical sponsor, we will provide you with a set of different weather input factors, e.g. wind direction, with which you are to forecast the power generation of a wind power plant portfolio. You may participate individually or as a team. The data input is organised in a realistic setting. 




## 2021-08

1. Prediction Interval Quality
2. ~~Multiplicative Decompositions (log transform ?)~~. 2022-05-09 OK (#178 : https://github.com/antoinecarme/pyaf/issues/178)
3. ~~PyTorch~~ : 2022-05-09 OK (#199 : https://github.com/antoinecarme/pyaf/issues/199)