Update README.md

forecast_input = [
np.sin(np.linspace(0, 20, 100))
np.sin(np.linspace(0, 20, 200)),
np.sin(np.linspace(0, 20, 400)),
]
--- >
forecast_input = [
np.sin(np.linspace(0, 20, 100)),
np.sin(np.linspace(0, 20, 200)),
np.sin(np.linspace(0, 20, 400)),
]

README.md

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