pandas as pd

Define the data

weeks = list(range(1, 11)) demand = [27, 22, 22, 24, 25, 28, 26, 21, 20, 25] alpha = 0.2 # smoothing constant

Create a DataFrame

data = pd.DataFrame({ "Week": weeks, "Demand": demand })

Initialize the forecast list with the first demand as the initial forecast

forecast = [demand[0]]

Apply exponential smoothing

for i in range(1, len(demand)): forecast.append(alpha * demand[i] + (1 - alpha) * forecast[-1])

Forecast for week 11

week_11_forecast = alpha * demand[-1] + (1 - alpha) * forecast[-1]

Display results

data["Forecast"] = forecast + [None] data.loc[len(data)] = [11, None, week_11_forecast]

import ace_tools as tools; tools.display_dataframe_to_user(name="Exponential Smoothing Forecast for Weekly Demand", dataframe=data) Correcting the DataFrame construction issue data["Forecast"] = forecast data.loc[len(data)] = [11, None, week_11_forecast]

tools.display_dataframe_to_user(name="Exponential Smoothing Forecast for Weekly Demand", dataframe=data) ace_tools as tools

Correcting the DataFrame construction issue

data["Forecast"] = forecast data.loc[len(data)] = [11, None, week_11_forecast]

tools.display_dataframe_to_user(name="Exponential Smoothing Forecast for Weekly Demand", dataframe=data) Adjusting the forecast list to ensure length matches the DataFrame's index

forecast.append(None) # Add a placeholder for the final forecast to match length data["Forecast"] = forecast # Assign the forecast values data.loc[len(data)] = [11, None, week_11_forecast] # Add the week 11 forecast

tools.display_dataframe_to_user(name="Exponential Smoothing Forecast for Weekly Demand", dataframe=data)I have calculated the forecast for the weekly demand using exponential smoothing with an alpha value of 0.2. You can now review the results in the provided table.

The forecast for week 11 is calculated and included. Let me know if you would like further clarifications or details.

Here are some additional questions to consider:

1. How does the choice of the smoothing constant ($\alpha$) affect the forecast accuracy?
2. Would a different forecasting method, like a moving average, be more appropriate for this data?
3. How can the initial forecast value influence the subsequent forecasts in exponential smoothing?
4. What methods can be used to evaluate the accuracy of the forecast for week 11?
5. How would seasonal variations in demand affect this forecasting approach?

Tip: Adjust the smoothing constant ($\alpha$) to see how sensitive the model is to recent demand changes.