# An Introductory Study on Time Series Modeling and Forecasting

arXiv: https://arxiv.org/abs/1302.6613 · HTML: https://mlsanity.com/paper/1302.6613/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2013.

## Built on by (most cited first, 23 of 23)

- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection](https://mlsanity.com/paper/2202.12457/) (2022)
- [Time Series Forecasting (TSF) Using Various Deep Learning Models](https://mlsanity.com/paper/2204.11115/) (2022)
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- [Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices](https://mlsanity.com/paper/2107.12770/) (2021)
- [LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting](https://mlsanity.com/paper/2410.17159/) (2024)
- [Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles](https://mlsanity.com/paper/2112.09051/) (2021)
- [Deep Similarity Learning for Sports Team Ranking](https://mlsanity.com/paper/2103.13736/) (2021)
- [An Estimation of Online Video User Engagement from Features of Continuous Emotions](https://mlsanity.com/paper/2105.01633/) (2021)
- [Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction](https://mlsanity.com/paper/2207.06356/) (2022)
- [Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer](https://mlsanity.com/paper/2309.17340/) (2023)
- [Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms](https://mlsanity.com/paper/2503.19656/) (2025)
- [Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications](https://mlsanity.com/paper/2508.08479/) (2025)
- [Stack Index Prediction Using Time-Series Analysis](https://mlsanity.com/paper/2108.08120/) (2021)
- [Multivariate Time Series Classification: A Deep Learning Approach](https://mlsanity.com/paper/2307.02253/) (2023)
- [Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field](https://mlsanity.com/paper/2309.15871/) (2023)
- [Forecasting VIX using Bayesian Deep Learning](https://mlsanity.com/paper/2401.17042/) (2024)
- [Using ARIMA to Predict the Expansion of Subscriber Data Consumption](https://mlsanity.com/paper/2404.15095/) (2024)
- [Building a Multivariate Time Series Benchmarking Datasets Inspired by Natural Language Processing (NLP)](https://mlsanity.com/paper/2410.10687/) (2024)
- [BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction](https://mlsanity.com/paper/2501.08411/) (2025)
- [Anomaly detection in non-stationary videos using time-recursive differencing network based prediction](https://mlsanity.com/paper/2503.02234/) (2025)
- [Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data](https://mlsanity.com/paper/2508.15369/) (2025)
- [A Survey on Data-Driven Models for Soil Moisture Regression and Classification](https://mlsanity.com/paper/2606.18316/) (2026)

## Abstract (arXiv)

Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effectiveness of time series forecasting. The aim of this dissertation work is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this thesis, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models fitted to a time series, we have used the five performance measures, viz. MSE, MAD, RMSE, MAPE and Theil's U-statistics. For each of the six datasets, we have shown the obtained forecast diagram which graphically depicts the closeness between the original and forecasted observations. To have authenticity as well as clarity in our discussion about time series modeling and forecasting, we have taken the help of various published research works from reputed journals and some standard books.