Time Series Modeling for Forecasting: A Practical Framework for Data-Driven Temporal Analysis in Machine Learning

Author: Debabrata Pruseth
Publication Date: 2025/10/23 
Document Type: Technical Note / Research Article
Language: English

Abstract

Time series data represents one of the most widely occurring forms of real-world data, appearing in finance, retail, energy, healthcare, climate science, cybersecurity, manufacturing, and digital systems. Unlike independent tabular observations, time series data contains temporal dependency, where current values may depend on previous observations, seasonal cycles, trends, anomalies, and external contextual variables. This research presents a practical framework for time series modeling and forecasting in machine learning. The framework organizes time series problems into forecasting, classification, regression, clustering, anomaly detection, segmentation, and similarity search. It further distinguishes univariate, multivariate, local, and global modeling strategies. The methodology covers preprocessing, feature extraction, sliding-window transformation, statistical forecasting, machine learning models, deep learning architectures, pre-trained time series models, and emerging time series foundation models. The study emphasizes that effective forecasting depends not only on algorithm selection but also on problem framing, temporal validation, data preparation, feature design, model interpretability, and responsible use. The contribution of this work is a structured decision framework that connects classical methods such as ARIMA and exponential smoothing with modern approaches such as gradient boosting, temporal convolutional networks, transformers, N-BEATS, Temporal Fusion Transformers, Chronos, TimesFM, TimeGPT, and other foundation-model approaches. The study does not report new benchmark experiments; instead, it provides a practical, research-oriented methodology for applied temporal analysis.

Keywords
Time Series Forecasting, Time Series Modeling, Machine Learning, Temporal Analysis, ARIMA, Exponential Smoothing, Feature Engineering, Forecasting Framework, Deep Learning, Transformers, Temporal Fusion Transformer, N-BEATS, Temporal Convolutional Networks, Chronos, TimesFM, TimeGPT, Foundation Models, Predictive Analytics, Multivariate Time Series, Anomaly Detection, Data Science

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Time Series Modeling for Forecasting: A Practical Framework for Data-Driven Temporal Analysis in Machine Learning

Suggested Citation
Pruseth, D. (2026). Time Series Modeling for Forecasting: A Practical Framework for Data-Driven Temporal Analysis in Machine Learning. Debabrata Pruseth AI blog.

Companion Note
This page provides the abstract and full-text PDF for the research version of the article. A companion blog post explains the same work in a more narrative and implementation-focused style.

Read the companion blog:
https://debabratapruseth.com/a-beginners-guide-to-time-series-modeling/