Comments (2)
DAILY
A very common seasonality for daily data is 7 because there are common patterns that repeat every week.
Particularly if your data comes from human economic activities.
Of course there are 'natural' seasonalities for daily data like 365, the length of the year, but usually you want to train your model with shorter input_sizes.
HOURLY
The list allows you to add multiple seasonalities at once for example with hourly data you may want both the daily seasonality and the weekly seasonality so you can set seasonality = [24, 24*7].
WEEKLY
Weekly is set to 52 because there are 52 weeks in the year.
Regarding your data if you have data recorded every six months, your seasonality can be 2. At the end of the day the model you select should follow empirical performance.
from esrnn_torch.
Ok got it
thanks a lot
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Related Issues (20)
- Example of use of the 'unique_id' and 'x' variables. HOT 1
- Very short time series in unbalanced panel, assert n_windows>0 HOT 3
- application for time series data with trend HOT 1
- Saving Model HOT 1
- Why is numpy==1.16.1 the only numpy version compatible with ESRNN? HOT 1
- AssertionError: clean np.nan's from unique_idxs unbalanced Y_df HOT 10
- Save functionality broken; ESRNN Model Object structure not reflected in save & load
- Exogenous variables HOT 1
- Allow for more covariates / features HOT 1
- ES-RNN for long sequence time series forecasting? HOT 1
- Prediction interval for ES-RNN HOT 5
- Data Formatting for ES-RNN HOT 1
- Hourly results typo in the results table in readme? HOT 2
- input format of training and test data HOT 1
- Input for OWA Calculation - AttributeError HOT 2
- how to use continuous exogenous variable in the future for forecasting problem HOT 1
- Return NaN prediction
- How to prepare the data format? HOT 12
- problem with pandas HOT 1
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