Simple Exponential Smoothing
import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.api import SimpleExpSmoothing df = pd.read_csv('/content/all_stocks_5yr.csv') apple_df = df[df["Name"] == "AAPL"].copy() apple_df["date"] = pd.to_datetime(apple_df["date"]) apple_df.sort_values("date", inplace=True) apple_df.set_index("date", inplace=True) apple_df = apple_df.asfreq('B') apple_df["close"] = apple_df["close"].interpolate() apple_close = apple_df["close"] plt.figure(figsize=(10, 4)) plt.plot(apple_close, label="Apple Closing Price", color="black") plt.title("Apple Stock Closing Prices") plt.xlabel("Date") plt.ylabel("Price") plt.legend() plt.grid(True) plt.show() 
fit1 = SimpleExpSmoothing(apple_close).fit( smoothing_level=0.2, optimized=False ) fcast1 = fit1.forecast(60).rename(r"$\alpha=0.2$") fit2 = SimpleExpSmoothing(apple_close).fit() fcast2 = fit2.forecast(60).rename(r"$\alpha=%s$" % fit2.model.params["smoothing_level"]) # Plot actual data plt.figure(figsize=(12, 6)) plt.plot(apple_close, marker="o", color="black", markersize=2, linestyle="-", linewidth=1, label="Actual Prices") # Plot fitted values plt.plot(fit1.fittedvalues, linestyle="--", color="blue", linewidth=1) plt.plot(fit2.fittedvalues, linestyle="--", color="red", linewidth=1) #plt.plot(fit3.fittedvalues, linestyle="--", color="green", linewidth=1) # Forecast lines (thicker & different markers) (line1,) = plt.plot(fcast1, marker="s", color="blue", markersize=4, linestyle="-", linewidth=2, label=fcast1.name) (line2,) = plt.plot(fcast2, marker="s", color="red", markersize=4, linestyle="-", linewidth=2, label=fcast2.name) # Highlight forecast area plt.axvspan(fcast1.index[0], fcast1.index[-1], color="gray", alpha=0.2, label="Forecast Period") # Add legend plt.legend(loc="upper left") plt.title("Simple Exponential Smoothing - Forecast vs Actual") plt.xlabel("Date") plt.ylabel("Stock Price") plt.grid(True, linestyle="--", alpha=0.5) # Light grid for better readability plt.show() 
