Update README.md
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ImMahdi
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README.md
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@@ -77,6 +77,7 @@ We provide APIs to forecast from either array inputs or `pandas` dataframe. Both
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In particular, regarding the frequency, TimesFM expects a categorical indicator valued in {0, 1, 2}:
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- **0** (default): high frequency, long horizon time series. We recommend using this for time series up to daily granularity.
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- **1**: medium frequency time series. We recommend using this for weekly and monthly data.
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- **2**: low frequency, short horizon time series. We recommend using this for anything beyond monthly, e.g. quarterly or yearly.
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@@ -97,7 +98,7 @@ Array inputs, with the frequencies set to low, medium, and high respectively.
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```python
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import numpy as np
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forecast_input = [
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np.sin(np.linspace(0, 20, 100))
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np.sin(np.linspace(0, 20, 200)),
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np.sin(np.linspace(0, 20, 400)),
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]
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In particular, regarding the frequency, TimesFM expects a categorical indicator valued in {0, 1, 2}:
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+
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- **0** (default): high frequency, long horizon time series. We recommend using this for time series up to daily granularity.
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- **1**: medium frequency time series. We recommend using this for weekly and monthly data.
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- **2**: low frequency, short horizon time series. We recommend using this for anything beyond monthly, e.g. quarterly or yearly.
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```python
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import numpy as np
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forecast_input = [
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np.sin(np.linspace(0, 20, 100)),
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np.sin(np.linspace(0, 20, 200)),
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np.sin(np.linspace(0, 20, 400)),
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]
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