Skill Guide

Time-series forecasting with ARIMA, Prophet, and LSTM architectures

The application of statistical (ARIMA), decomposable (Prophet), and deep learning (LSTM) model families to predict future values based on historically timestamped, sequential data.

This skill is critical for data-driven operational planning, inventory optimization, and financial modeling. Directly translates to improved forecast accuracy, reduced operational costs, and enhanced strategic decision-making.

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9.1 Avg Demand

25% Avg AI Risk

How to Learn Time-series forecasting with ARIMA, Prophet, and LSTM architectures

1. Master time-series components: trend, seasonality, cyclicity, and noise. 2. Understand stationarity, differencing, and ACF/PACF plots for ARIMA parameter selection (p, d, q). 3. Learn Python data handling with Pandas (datetime indexing, resampling) and basic visualization with Matplotlib/Seaborn.

1. Implement ARIMA with `statsmodels.tsa.arima.model.ARIMA` and diagnose residuals. 2. Use Facebook Prophet (`prophet.Prophet`) for business forecasting, tuning hyperparameters like `changepoint_prior_scale`. 3. Build a basic LSTM with Keras/TensorFlow for a univariate series, focusing on data windowing and normalization. Common mistake: ignoring the autocorrelation in residuals after ARIMA fitting.

1. Architect hybrid models (e.g., ARIMA for linear components + LSTM for non-linear residuals). 2. Integrate exogenous variables (ARIMAX, Prophet's regressors) and implement probabilistic forecasting. 3. Design end-to-end MLOps pipelines for automated model retraining, monitoring for data/concept drift, and A/B testing forecast models in production.

Practice Projects

Beginner

Project

Retail Store Weekly Sales Forecasting

Scenario

Forecast the next 12 weeks of sales for a single retail store using historical weekly data (CSV with 'date' and 'sales' columns).

How to Execute

1. Load data, parse dates, set datetime index. 2. Split data: last 12 weeks as test set. 3. Fit an ARIMA(1,1,1) model on training data. 4. Generate forecast, calculate MAPE against test set. 5. Visualize forecast vs. actuals.

Intermediate

Project

Multi-Product Demand Forecasting with Event Impact

Scenario

Forecast demand for 5 different products in an e-commerce setting, accounting for promotional events (e.g., Black Friday) as known regressors.

How to Execute

1. Use Prophet for each product series, incorporating promotional dates as `holidays`. 2. Engineer lag features and rolling statistics as additional regressors. 3. Compare Prophet's accuracy against a baseline ARIMA. 4. Implement a simple backtesting function to evaluate performance across multiple time windows.

Advanced

Project

Anomaly Detection & Forecasting in Server Metrics

Scenario

Build a system to monitor CPU utilization across a server cluster, forecast future load to predict potential outages, and flag real-time anomalies.

How to Execute

1. Train an LSTM autoencoder on normal operational data to learn reconstruction error. 2. Use the LSTM's reconstruction error as an anomaly score for live data. 3. For forecasting, train a separate Seq2Seq LSTM model. 4. Deploy both models via a REST API (e.g., FastAPI) with a monitoring dashboard (Grafana).

Tools & Frameworks

Software & Platforms

Python (pandas, numpy, scikit-learn)statsmodels (ARIMA)Facebook Prophet (prophet)TensorFlow/Keras (LSTM)Darts (unified forecasting library)

Core stack: Pandas for data prep, statsmodels for ARIMA, Prophet for quick business-oriented forecasting with events, TensorFlow/Keras for building custom LSTM architectures. Darts simplifies comparison across model families.

Evaluation & Deployment

MAPE, RMSE, SMAPE (metrics)MLflow (experiment tracking)FastAPI (model serving)Grafana (monitoring dashboards)

Use symmetric metrics like SMAPE to avoid division-by-zero issues. Track all experiments and hyperparameters with MLflow. Serve models via lightweight APIs and build dashboards to monitor forecast accuracy and drift in production.

Interview Questions

Answer Strategy

Demonstrate a structured, iterative approach to model identification. Sample answer: 'I would first difference at the seasonal lag (4 for quarters) to remove seasonality, examining the ACF/PACF of the differenced series. If both cut off sharply, I'd start with a SARIMA model (p, d, q)(P, D, Q)s. I would fit several candidate models, check AIC/BIC, and rigorously validate by ensuring residuals are white noise via the Ljung-Box test.'

Answer Strategy

Tests ability to debug deep learning models and apply regularization. Sample answer: 'This is classic overfitting. I'd first verify my validation set is truly out-of-sample and check for data leakage. Then, I'd implement a systematic reduction of model complexity (fewer layers/units), introduce dropout layers, apply early stopping, and potentially use a simpler architecture. I'd also verify if the model is simply memorizing noise by visualizing predictions on the validation set.'