Complete Time Series Project Tutorial

Step-by-step walkthrough of building a time series forecasting system.

Table of Contents

• Project Overview
• Step 1: Load and Explore Data
• Step 2: Check Stationarity
• Step 3: Build ARIMA Model
• Step 4: Build LSTM Model
• Step 5: Compare Models

Project Overview

Project: Stock Price Forecasting

Dataset: Stock price data

Goals: Forecast future prices using ARIMA and LSTM

Step 1: Load and Explore Data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('stock_prices.csv', index_col='Date', parse_dates=True)
ts = df['Close']

# Explore
print(ts.describe())
ts.plot(figsize=(14, 6))
plt.title('Stock Prices Over Time')
plt.show()

Step 2: Check Stationarity

from statsmodels.tsa.stattools import adfuller

def check_stationarity(ts):
    result = adfuller(ts.dropna())
    return result[1] <= 0.05

is_stati>
if not is_stationary:
    ts = ts.diff().dropna()

Step 3: Build ARIMA Model

from pmdarima import auto_arima

model = auto_arima(ts, seasonal=True, m=12)
forecast = model.predict(n_periods=30)

Step 4: Build LSTM Model

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Prepare sequences
X, y = create_sequences(ts_scaled, seq_length=10)

# Build and train
model = Sequential([LSTM(50), Dense(1)])
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=50)

Step 5: Compare Models

# Evaluate both models
arima_rmse = calculate_rmse(y_test, arima_forecast)
lstm_rmse = calculate_rmse(y_test, lstm_forecast)

print(f"ARIMA RMSE: {arima_rmse:.4f}")
print(f"LSTM RMSE: {lstm_rmse:.4f}")

Congratulations! You've built a complete time series forecasting system!