Python for Finance & Insurance: Quick Wins for Analysts by Keshvari Arya

Author:Keshvari, Arya
Language: eng
Format: epub
Published: 2025-04-03T00:00:00+00:00

# Load the dataset

file_path = 'datasets/market_trends.csv'

df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date')

# Plot the historical data to visualize the trend

plt.figure(figsize=(10, 5))

plt.plot(df.index, df['Close'], label='Historical Close Price')

plt.title('Market Trend Data: MSFT')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.grid(True)

plt.show()

Forecasting with ARIMA (and SARIMA for Seasonality)

ARIMA models capture temporal dependencies in time series data by using past values (Autoregressive - AR), differencing to make the series stationary (Integrated - I), and past forecast errors (Moving Average - MA).

The order parameter, order=(p, d, q), defines the ARIMA model structure:

p: Number of lag observations included (AR part).

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