A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

Saima Mustafa · Shafqat Iqbal · Sajawal Piracha

39 citations0/0 files storedJournal of Computational and Cognitive Engineering

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Fuzzy Logic and Control Systems Time Series Analysis and Forecasting

Abstract

A time series is a sequence of elements with numerical data in sequential order and having regular intervals. Time series are used in statistics, enrollments, signal processing, econometrics, mathematical finance and weather forecasting, etc. It helpsus to forecast and predict the time series data in different domains. There are many methods to forecast the enrollments in literature which have large applications and are presented in the field of statistics and econometrics. One of the robust methods, we used in our research is moving average. It helps to forecast and predict the data whenever the fuzziness occurs in time series data, which is not appropriate in crisp time series forecasting. To get rid of this problem, the fuzzy interval partitioning method proved to be more appropriate to generate accurate results. This research will focus to overcomethe failure of the crisp method and to show the use of a fuzzy interval partitioning method. The fuzzy interval partitioning methodis different from another interval partition schemes because it specifies the linguistic values rather than numerical values. It is also used todeal with uncertain conditions. So, fuzzy interval partitioning improves data utilization and also calculate the higher predicted accuracy rate. Besides this research, we use a quantitative method and a fuzzy moving average with the interval partitioning method. Then we compare the efficiency of moving average model and moving average with fuzzy interval partitioning method for forecasting the enrollments. Received: 15 January 2022 | Revised: 8 March 2022 | Accepted: 16 March 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 39 citations exist upstream.

References

1965Fuzzy sets 1993Forecasting enrollments with fuzzy time series — Part I 1996Forecasting enrollments based on fuzzy time series 2001Effective lengths of intervals to improve forecasting in fuzzy time series 2004Weighted fuzzy time series models for TAIEX forecasting 2008A new approach for determining the length of intervals for fuzzy time series 2016Fuzzy time series forecasting method based on hesitant fuzzy sets 2007Deterministic fuzzy time series model for forecasting enrollments

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.