Roadmap

🔰 Phase 1: Foundations of Time Series ✅ Topics:

What is a Time Series?

Components of Time Series:

    Trend

    Seasonality

    Cyclicity

    Noise (residuals)

Time Series vs Cross-Sectional Data

Stationarity & White Noise

✅ Math/Stats:

Basic statistics: mean, median, variance

Covariance, autocorrelation

Lag and difference operations

✅ Tools:

Python, Jupyter Notebooks

Libraries: pandas, matplotlib, seaborn

✅ Practice:

import pandas as pd
import matplotlib.pyplot as plt

ts = pd.read_csv("air_passengers.csv", parse_dates=['Month'], index_col='Month')
ts.plot()
plt.show()

📊 Phase 2: Exploratory Time Series Analysis (TSA) ✅ Topics:

Plotting time series data

Rolling statistics (moving average, std)

ACF (Autocorrelation Function) & PACF (Partial ACF)

Seasonal decomposition (additive vs multiplicative)

✅ Tools:

statsmodels.tsa, scipy.signal

✅ Code Sample:

from statsmodels.tsa.seasonal import seasonal_decompose

result = seasonal_decompose(ts, model='multiplicative')
result.plot()

🧪 Phase 3: Time Series Preprocessing ✅ Topics:

Handling missing timestamps

Resampling (up-sampling, down-sampling)

Lag features, rolling features

Differencing to remove trend/seasonality

Stationarity check (ADF/KPSS test)

✅ Code:

from statsmodels.tsa.stattools import adfuller

adf_result = adfuller(ts['#Passengers'])
print(f'p-value: {adf_result[1]}')

🔁 Phase 4: Classical Time Series Models ✅ Models:

AR (AutoRegressive)

MA (Moving Average)

ARMA, ARIMA (AutoRegressive Integrated Moving Average)

SARIMA / SARIMAX (seasonal ARIMA + exogenous vars)

Exponential Smoothing (ETS)

✅ Concepts:

Model Order Selection (AIC/BIC)

Model Diagnostics

Forecasting & Confidence Intervals

✅ Tools:

statsmodels.tsa.arima_model, pmdarima

✅ Code:

from pmdarima import auto_arima

model = auto_arima(ts, seasonal=True, m=12)
model.summary()

🧠 Phase 5: Machine Learning for Time Series ✅ Techniques:

Train/Test split in time series (walk-forward validation)

Feature engineering (lag/rolling windows)

ML Models: Linear Regression, Random Forest, XGBoost

Multi-step forecasting

✅ Tools:

scikit-learn, xgboost, lightgbm

✅ Code:

from sklearn.ensemble import RandomForestRegressor

# Create lag features
df['lag1'] = df['value'].shift(1)
df.dropna(inplace=True)

model = RandomForestRegressor()
model.fit(df[['lag1']], df['value'])

🧠 Phase 6: Deep Learning for Time Series ✅ Models:

RNN

LSTM (Long Short-Term Memory)

GRU (Gated Recurrent Unit)

1D CNNs for time series

Transformer-based models (e.g., Time2Vec, Temporal Fusion Transformer)

✅ Tools:

TensorFlow/Keras, PyTorch

✅ Code Sample:

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

model = Sequential()
model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=10)

🔍 Phase 7: Model Evaluation & Forecasting ✅ Metrics:

MAE, RMSE, MAPE

Cross-validation for time series (e.g., TimeSeriesSplit)

Visualize forecast vs actual

Prediction intervals

✅ Code:

from sklearn.metrics import mean_absolute_error

mae = mean_absolute_error(y_true, y_pred)
print(f'Mean Absolute Error: {mae}')

🗂️ Phase 8: Advanced Topics ✅ Topics:

Anomaly Detection in Time Series

Time Series Clustering

Multivariate Time Series

Exogenous variables (SARIMAX, VAR)

Forecasting with missing data

Probabilistic Forecasting

✅ Libraries:

Facebook Prophet

NeuralProphet

darts (uniting classical + DL models)

GluonTS, Kats, Nixtla

🚀 Phase 9: Project Ideas

Stock Price Forecasting (with LSTM & ARIMA)

Electricity Load Forecasting

Sales Forecasting for E-Commerce

Weather Time Series Analysis

Air Quality Index (AQI) Forecasting

IoT Sensor Time Series Monitoring

📚 Recommended Resources Books:

"Practical Time Series Forecasting" – Galit Shmueli

"Time Series Analysis and Forecasting" – Brockwell & Davis

"Deep Learning for Time Series Forecasting" – Jason Brownlee

Courses:

Coursera – Time Series Forecasting

Udemy – Python for Time Series Data Analysis

fast.ai Time Series Course

🛠️ Final Tools Mastery Checklist Tool Use pandas Time Series handling matplotlib/seaborn Plotting statsmodels ARIMA, SARIMA pmdarima Auto model selection scikit-learn ML pipelines keras/pytorch Deep learning darts, prophet High-level time series models