scholarly journals Predicting Total Business Sales using Time Series Analysis

2020 ◽  
pp. 475-482
Author(s):  
Navya Sri Kalli ◽  
Harsha Teja Pullagura
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Models ◽  
Key Factors ◽  
Sales Data ◽  
Grand Total ◽  
Simple Exponential Smoothing ◽  
Total Sales

aEconomic activity undergoes 4 phases (expansion, peak, contraction, trough/recession) in which recession is a period of lowest activity and peak indicates the highest activity. Total Business sales is one of the key factors that influence the economic activity of a country. Total sales or gross sales is the grand total of all sales revenues a business generates from normal activities. The frequency of time series sales data can be monthly, quarterly, or annually. Prediction of business sales is highly important as it determines various factors in the market including Gross Domestic Product (GDP). The algorithms or models required for prediction of time series data are different from other machine learning models. Since sales is affected by time, a time series data should be stationary. Only when the data is stationarized, we can apply the algorithms on them. In this paper, monthly sales data is collected and predictions are done using moving average, simple exponential smoothing, Holt’s model, ARIMA, and SARIMAX. Root Mean Square(RMS) is the accuracy metric of time series models and lower RMS indicates higher accuracy. In this paper, a lower value of RMS is obtained for the SARIMAX model.

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

Hidden periodic autoregressiye-moving average models in time series data

Biometrika ◽  
1980 ◽  
Vol 67 (2) ◽  
pp. 365-373 ◽  
Author(s):  
G. C. TIAO ◽  
M. R. GRUPE
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Moving Average Models

A UNIFIED APPROACH TO THE MEASUREMENT ERROR PROBLEM IN TIME SERIES MODELS

2002 ◽  
Vol 18 (2) ◽  
pp. 278-296 ◽  
Author(s):  
Katsuto Tanaka
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Measurement Error ◽  
Long Memory ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Models ◽  
Unified Approach ◽  
Underlying Process ◽  
Short Memory

The measurement error problem that we consider in this paper is concerned with the situation where time series data of various kinds—short memory, long memory, and random walk processes—are contaminated by white noise. We suggest a unified approach to testing for the existence of such noise. It is found that the power of our test crucially depends on the underlying process.

Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

2014 ◽  
Vol 955-959 ◽  
pp. 863-868
Author(s):  
Rong Yu ◽  
Bo Feng Cai ◽  
Xiang Qin Su ◽  
Ya Zi He ◽  
Jing Yang
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Research Areas ◽  
Noaa Avhrr ◽  
Index Time Series

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

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