# Time Series Prediction

Some details specific to time series:

- Feature engineering is difficult and should be handled by care (can easily learn bias - i.e. trends, seasonality, accumulation)
- Data is much smaller
- A different algorithm is needed
- extrapolation
- confidence interval

- Residuals should represent white noise
- no correlation
- zero mean and constant variance

- Resolution matters
- prepare data and predict very granular then predictions can be aggregated,
for example: predict use and predict
`hourly`

then take average for`daily`

- prepare data and predict very granular then predictions can be aggregated,
for example: predict use and predict
- Confidence intervals:
- keep some part of the data aka. dropout and run simulations
- kernel mixture network

- Some models might have bias
- have high accurary or high error (black or gray swan according to the nature of the series)

Models to be tested in the order of complexity (to have a baseline and to iterate):

- simple hand-made formula: average the data
- statistical model: autoregression such as SARIMA, linear sum of old observations
- exponential smoothing or winter-holt: this time exponential functions instead of linear, forget old observations at an exponentially decreasing reate
- simple neural network
- advanced neural network: LSTM, GRU, etc.