Forecasting of Cotton Production in India using Advanced Time Series Models

2020 ◽  
pp. 583-590
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Data Series ◽  
Time Series Models ◽  
Annual Data ◽  
Cotton Production ◽  
Out Of Sample ◽  
Augmented Dickey Fuller ◽  
Logarithmic Series ◽  
Best Fit

Reliable and timely estimates of cotton production are important providing useful inputs to policymakers for proper foresighted and informed planning. So an attempt was made to forecast the production of cotton at all India level using a time series model. The annual data on production of cotton for the period 1951-52 to 2018-19 was processed. The data were transformed into logarithmic series to stabilize the variance of the series. The stationarity of the data was checked with the help of the Augmented Dickey-Fuller and Phillips-Perron tests. The results of ADF and PP tests confirmed the cotton production series was non-stationary at level, so stationarity in the data was brought by differencing the data series at a first lag. The pattern present in ACF and PACF and results of SCAN and ESACF provided guideline to select the order of non-seasonal ARIMA model. The best fit ARIMA model (ARIMA: 3 1 1) was selected based on AIC criteria and residual diagnostic. The performance of the model was judged based on the MAPE value. The out of sample forecast of cotton production at all India level was carried out for the period 2019-20 to 2021-22. The forecasted values indicated a slight increase in the production of cotton compared to 2018-19.

scholarly journals The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE

2021 ◽  
Vol 6 (3) ◽  
pp. 22-33
Author(s):  
Atiqa Nur Azza Mahmad Azan ◽  
Nur Faizatul Auni Mohd Zulkifly Mototo ◽  
Pauline Jin Wee Mah
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Models ◽  
Monthly Average ◽  
Arfima Models ◽  
Gold Market ◽  
Univariate Time Series ◽  
The World ◽  
Arfima Model ◽  
Best Fit

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

scholarly journals Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
BALJEET KAUR ◽  
NAVNEET KAUR ◽  
K. K. GILL ◽  
JAGJEEVAN SINGH ◽  
S. C. BHAN ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Goodness Of Fit ◽  
Time Series Data ◽  
Arima Model ◽  
Kendall Test ◽  
Series Data ◽  
Data Series ◽  
Augmented Dickey Fuller ◽  
Minimum Air Temperature

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

scholarly journals An Analysis of Time-Series Models for Age-Specific Mortality Rates in India

2020 ◽  
Vol 5 (8) ◽  
pp. 1133-1140
Author(s):  
Aritra Sen ◽  
Shalmoli Dutta
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Current Trend ◽  
Mortality Rates ◽  
Percentage Error ◽  
Time Series Models ◽  
Annual Data ◽  
Registration System ◽  
The Mean ◽  
Best Fit

Mortality is a continuous force of attrition, tending to reduce the population, a prime negative force in the balance of vital processes (Bhasin and Nag, 2004). Sample Registration System (SRS) serves as the only source of annual data on vital events on a full scale from 1969-70 in India. Few studies have examined the trends and patterns of mortality across time and regions in India (Preston and Bhat, 1984). The Under 5 Mortality Rates (U5MR) can be seen to decrease by more than half from 1970 to 2017 but in contrast little is known about the mortality patterns of the older children (5-9) and young adolescents (10-14), and not many studies have been done on their changing trends (Masquelier et al., 2018). Using the annual data for the 5-14 age, the trend of decline in the mortality patterns is studied from 1970 to 2013. The linear trend in the time series plot suggests analysis using time series models AR(p), MA(q), ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random Walk with drift models to get the best fit to the trend of the data. The order of the time series models have been calculated by studying the ACF, PACF plots and the coefficients have been derived using the Yule-Walker equation matrix. An in-sample forecast of the years 2014-17 are taken. The Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) as a measure of accuracy is used to determine the best fit model. ARIMA(3,1,1) produced lower values making it the best-fit model. Out-of-sample forecasting was done for 2018-2025. The forecast value shows that at the current trend, India would have 0.03 deaths per 1000 population in the 5-14 age group in 2025 showing that the government’s policies and health care interventions towards realization of the MDG4 goal is working positively.

scholarly journals Forecasting Yearling Prices: A Comparison Of Alternative Time Series Models

2011 ◽  
Vol 10 (3) ◽  
pp. 17
Author(s):  
James E. Payne ◽  
Robert R. Sharp ◽  
Susan A. Simmons
Keyword(s):  
Time Series ◽  
North America ◽  
Structural Model ◽  
Arima Model ◽  
The Other ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Out Of Sample ◽  
Hard Times ◽  
One Step

The thoroughbred breeding industry in North America has fallen on hard times. The health of this industry is often gauged by prices obtained for yearlings at North American auctions, particularly the average prices of summer sales at Keeneland and Saratoga. We examine various exponential smoothing algorithms along with a market-based structural model, as well as an ARIMA model in generating one-step ahead forecasts. The market-based structural model outperforms the other approaches with respect to both in- and out-of-sample forecasting accuracy.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals Forecasting Monthly Gold Prices using ARIMA Model: Evidence from Indian Gold Market

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1373-1376
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Gold Price ◽  
Data Series ◽  
Suitable Model ◽  
Natural Logarithm ◽  
Price Series ◽  
Gold Market ◽  
Sample Data ◽  
Augmented Dickey Fuller

In this paper an attempt has been made to give an overview of the Indian gold market so as to develop a model enabling the forecast of gold prices in India. One troy ounce is equal to 31.103 grams. The monthly sample data of gold price (in INR per troy ounce) is taken from December 1997 to December 2017.The entire data has been divided into two segments for estimation and validation sample and to find out the efficiency and accuracy of forecasting models. Since the gold price data series have shown much deviation after March 2006 the first segment of the data is taken from the time period of December 1997 to March 2006 and second segment from April 2006 to December 2017.Due to a larger value and a huge time span of the sample data, the natural logarithm of gold price has been taken to conduct the study and build an effective model to forecast future gold prices. The unit root tests of Augmented Dickey Fuller‖ and Philips Perron have been used to test the gold price series as stationary or non-stationary. It is observed that series are stationary at first difference in both the methods. At first difference the ACFs and PACFs were pattern less and statistically not significant. Box-Jenkins’s Autoregressive Integrated Moving Average of Box-Jenkins methodology has been used for developing a forecasting model of gold price in India. Different models of ARIMA have been used to obtain best suitable model for forecasting using Eviews software 10 for both time periods i.e., December 1997 to March 2006 & April 2006 to December 2017

scholarly journals Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks

2020 ◽  
Vol 6 (2) ◽  
pp. 137-148
Author(s):  
J. Oliver Muncharaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Recurrent Neural Network ◽  
Stock Prices ◽  
Arima Model ◽  
Time Series Models ◽  
Prediction Errors ◽  
The Creation

In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

scholarly journals Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models

2020 ◽  
Author(s):  
Alemayehu Argawu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Models ◽  
Study Finding ◽  
Exponential Smoothing ◽  
Time Series Models ◽  
African Countries ◽  
Mass Gatherings ◽  
Preparedness Planning ◽  
Best Fit

Background: COVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Cote-dIvoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries. Methods: In this study, the COVID 19 new cases data have been modeled and forecasted using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020. Results: From July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Cote dʹIvoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote-dIvoire and Ghana. The Algeria data was followed the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Cote-dIvoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. The Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constantan for Cameroon, Cote-dIvoire, Ghana and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020. Conclusion: This study was conducted with the current measures; the forecasts and trends obtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their airspaces, and the countries other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.

scholarly journals 691. Real-Time Local Influenza Forecasting Using Smartphone-Connected Thermometer Readings

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S249-S249
Author(s):  
Aaron Miller ◽  
Inder Singh ◽  
Sarah Pilewski ◽  
Vladimir Petrovic ◽  
Philip M Polgreen
Keyword(s):  
Time Series ◽  
Real Time ◽  
Forecast Error ◽  
National Level ◽  
Model Performance ◽  
State Level ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Out Of Sample ◽  
Influenza Activity

Abstract Background Information regarding influenza activity can inform clinical and public health activities. However, current surveillance approaches induce a delay in influenza activity reports (typically 1–2 weeks). Recently, we used data from smartphone connected thermometers to accurately forecast real-time influenza activity at a national level. Because thermometer readings can be geo-located, we used state-level thermometer data to determine whether these data can improve state-level surveillance estimates. Methods We used temperature readings collected by the Kinsa smart-thermometer and mobile device app to develop state-level forecasting models to predict real-time influenza activity (1–2 weeks in advance of surveillance reports). We used state-reported influenza-like illness (ILI) to represent state influenza activity for 48 US states with sufficient surveillance data. Counts of temperature readings, fever episodes and reported symptoms were computed by week. We developed autoregressive time-series models and evaluated model performance in an adaptive out-of-sample manner. We compared baseline time-series models containing lagged state-reported ILI activity to models incorporating exogenous thermometer readings. Results A total of 10,262,212 temperature readings were recorded from October 30, 2015 to March 29, 2018. In nearly all of the 48 states considered, weekly forecasts of ILI activity improved considerably when thermometer readings were incorporated. On average, state-level forecasting accuracy improved by 23.9% compared with baseline time-series models. In many states, such as PA, New Mexico, MA, Virginia, New York and SC, out-of-sample forecast error was reduced by more than 50% when thermometer data were incorporated. In general, forecasts were most accurate in states with the greatest number of device readings. During the 2017–2018 influenza season, the average improvement in forecast accuracy was 24.4%, and thermometer readings improved forecasting accuracy in 41, out of 48, states. Conclusion Data from smart thermometers accurately track real-time influenza activity at a state level. Local surveillance efforts may be improved by incorporating such information. Such data may also be useful for longer-term local forecasts. Disclosures I. Singh, Kinsa Inc.: Board Member, Employee and Shareholder, equity received and Salary. S. Pilewski, Kinsa Inc.: Employee and Shareholder, equity received and Salary. V. Petrovic, Kinsa Inc.: Employee and Shareholder, equity received and Salary.

scholarly journals STUDY OF THE INFLUENCE OF CHANGING THE PARAMETERS OF THE ARIMA MODEL ON THE QUALITY OF THE FORECAST FOR SHORT DATA SETS

2021 ◽  
Vol 21 (3) ◽  
pp. 36-46
Author(s):  
M.N. Fel’ker ◽  
◽  
V.V. Chesnov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Annual Data ◽  
Data Sets ◽  
Arima Models ◽  
Data Set ◽  
Series Analysis ◽  
The Future

Time series, i.e. data collected at various times. The data collection segments may differ de-pending on the task. Time series are used for decision making. Time series analysis allows you to get some result that will determine the format of the decision. Time series analysis was carried out in very ancient times, for example, various calendars became a consequence of the analysis. Later, time series analysis was applied to study and forecast economic, social and other systems. Time se-ries appeared a long time ago. Once upon a time, ancient Babylonian astronomers, studying the po-sition of the stars, discovered the frequency of eclipses, which allowed them to predict their appearance in the future. Later, the analysis of time series, in a similar way, led to the creation of various calen-dars, for example, harvest calendars. In the future, in addition to natural areas, social and economic ones were added. Aim. Search for classification patterns of time series, allowing to understand whether it is possible to apply the ARIMA model for their short-term (3 counts) forecast. Materials and methods. Special software with ARIMA implementation and all need services is made. We examined 59 data sets with a short length and step equal a year, less than 20 values in the paper. The data was processed using Python libraries: Statsmodels and Pandas. The Dickey – Fuller test was used to de-termine the stationarity of the series. The stationarity of the time series allows for better forecasting. The Akaike information criterion was used to select the best model. Recommendations for a rea-sonable selection of parameters for adjusting ARIMA models are obtained. The dependence of the settings on the category of annual data set is shown. Conclusion. After processing the data, four categories (patterns) of year data sets were identified. Depending on the category ranges of parame-ters were selected for tuning ARIMA models. The suggested ranges will allow to determine the starting parameters for exploring similar datasets. Recommendations for improving the quality of post-forecast and forecast using the ARIMA model by adjusting the settings are given.

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