Where Could Bitcoin's Price Go in the Next Two Weeks?

Walkthrough

1. Load it from the catalogue

   Find the table, read its recorded target and print the column names.

   import pandas as pd
   from welt import Client, Forecast
   
   with Client() as client:
       item = client.research_datasets(q="top 100 cryptocurrency 2020", limit=1)["items"][0]
       info = client.research_dataset(item["id"])
       data = client.load_research_dataset(item["id"])
   
   target = info["targets"][0]
   target = target["name"] if isinstance(target, dict) else target
   print([column["name"] for column in info["schema"]], target)

2. Pick the history

   Keep the time column and the target, for the series you want.

   time, series = "date", "name"
   history = data[data[series].isin(["Bitcoin", "Ethereum", "Litecoin"])][[series, time, target]].dropna()
   history[time] = pd.to_datetime(history[time])

3. Forecast with a range

   Ask for the next 14 steps, one per day. The median is the forecast, and the 10% and 90% columns give a low and a high case.

   model = Forecast(model="chronos-2").fit(history, time=time, id=series, target=[target])
   result = model.forecast(14, quantile_levels=[0.1, 0.5, 0.9])
   print(result.to_frame()[["id", "timestamp", "median", "q_0.1", "q_0.9"]].head(14))

What you get