How to perform time series analysis that contains multiple groups in Python using fbProphet or other models?

Question

All,

My dataset looks like following. I am trying to predict the \'amount\' for next 6 months using either the fbProphet or other model. But my issue is

Answer 1

I know this is old but I was trying to predict outcomes for different clients and I tried to use Aditya Santoso solution above but got into some errors, so I added a couple of modifications and finally this worked for me:

df = pd.read_csv('file.csv')
df = pd.DataFrame(df)
df = df.rename(columns={'date': 'ds', 'amount': 'y', 'client_id': 'client_id'})
#I had to filter first clients with less than 3 records to avoid errors as prophet only works for 2+ records by group
df = df.groupby('client_id').filter(lambda x: len(x) > 2)

df.client_id = df.client_id.astype(str)

final = pd.DataFrame(columns=['client','ds','yhat'])

grouped = df.groupby('client_id')
for g in grouped.groups:
    group = grouped.get_group(g)
    m = Prophet()
    m.fit(group)
    future = m.make_future_dataframe(periods=365)
    forecast = m.predict(future)
    #I added a column with client id
    forecast['client'] = g
    #I used concat instead of merge
    final = pd.concat([final, forecast], ignore_index=True)

final.head(10)