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Here’s a point-form style article titled "The Complete Guide to Time Series Models" — perfect for a Dev.to post:
? The Complete Guide to Time Series Models
Time series modeling is essential when working with data indexed in time order — think stock prices, weather patterns, or GDP growth.
Here’s your complete point-form guide to time series models — from classic methods to deep learning.
? What is a Time Series?
1. AR (AutoRegressive)
$$
Y_t = \phi_1 Y_{t-1} + \epsilon_t
\]
$$
2. MA (Moving Average)
$$
Y_t = \mu + \theta_1 \epsilon_{t-1} + \epsilon_t
\]
$$
3. ARMA (AR + MA)
? Exponential Smoothing Models
6. Simple Exponential Smoothing
9. Regression Models
12. RNN (Recurrent Neural Network)
17. Facebook Prophet
? The Complete Guide to Time Series Models
Time series modeling is essential when working with data indexed in time order — think stock prices, weather patterns, or GDP growth.
Here’s your complete point-form guide to time series models — from classic methods to deep learning.
? What is a Time Series?
- A sequence of data points collected or recorded at specific time intervals.
- Time is a crucial component — order matters.
- Examples: Daily temperature, monthly sales, hourly web traffic.
- Trend: Long-term upward or downward movement.
- Seasonality: Regular patterns (e.g., quarterly demand).
- Cyclic Patterns: Irregular cycles over years.
- Noise: Random variations that can’t be explained.
1. AR (AutoRegressive)
- Predicts current value based on past values.
- Example: AR(1):
$$
Y_t = \phi_1 Y_{t-1} + \epsilon_t
\]
$$
2. MA (Moving Average)
- Uses past forecast errors to predict future values.
- Example: MA(1):
$$
Y_t = \mu + \theta_1 \epsilon_{t-1} + \epsilon_t
\]
$$
3. ARMA (AR + MA)
- Combines autoregressive and moving average components.
- Works well for stationary data.
- Adds differencing to handle trends (non-stationary data).
- Notation: ARIMA(p, d, q)
- Adds seasonality terms to ARIMA.
- Notation: ARIMA(p, d, q)(P, D, Q)
? Exponential Smoothing Models
6. Simple Exponential Smoothing
- Best for data without trend/seasonality.
- Weighted average with exponentially decreasing weights.
- Captures trend with two equations: level and trend.
- Adds seasonality to Holt’s method.
- Supports both additive and multiplicative seasonality.
9. Regression Models
- Use lag features (e.g., t-1, t-2) as inputs to a regression algorithm.
- Algorithms: Linear Regression, Random Forest, XGBoost
- Robust to outliers; good for non-linear patterns.
- Non-parametric, similarity-based forecasts.
12. RNN (Recurrent Neural Network)
- Good at handling sequences — but suffers from vanishing gradients.
- Solves RNN limitations with memory gates.
- Popular for long-sequence forecasting.
- Simpler than LSTM, similar performance.
- Detects short-term patterns using convolutional filters.
- Powerful for long sequences.
- Attention mechanism allows parallel processing (e.g., Informer, Time Transformer).
17. Facebook Prophet
- Handles trend, seasonality, holidays.
- Very user-friendly API.
- Multivariate — forecasts multiple time series variables together.
- For dynamic systems; used in control systems, robotics.
- MAE: Mean Absolute Error
- RMSE: Root Mean Squared Error
- MAPE: Mean Absolute Percentage Error
- AIC/BIC: For model selection (esp. ARIMA)
- Always check for stationarity.
- Use rolling windows for validation.
- Don’t shuffle data randomly — respect time order.
- Use lag plots, ACF/PACF for pattern detection.
- Resample or decompose for trend/seasonality insights.
Python:
- statsmodels
- pmdarima
- prophet
- scikit-learn
- tslearn
- darts (supports classic, ML, and DL models)
- No one-size-fits-all model — start with ARIMA or Holt-Winters, then move to ML/DL as needed.
- Understand your data's behavior before choosing a model.
- Experiment, validate, and monitor in production — time series drift is real.