scholarly journals Innovative trend analysis of rainfall in relation to soybean productivity over western Maharashtra

2021 ◽  
Vol 23 (2) ◽  
pp. 228-235
Author(s):  
R.N. SINGH ◽  
SONAM SAH ◽  
GAURAV CHATURVEDI ◽  
BAPPA DAS ◽  
H. PATHAK
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Time Series Data ◽  
Rainfall Variability ◽  
Rank Correlation ◽  
Annual Rainfall ◽  
Series Data ◽  
Traditional Methods ◽  
Innovative Trend Analysis ◽  
Level Of Significance

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra.

scholarly journals Trend analysis of rainfall using Mann-Kendall, Sen’s slope, moving average and least square techniques for Ahmednagar district of Maharashtra

2021 ◽  
Vol 17 (1) ◽  
pp. 19-25
Author(s):  
Virendra N. Barai ◽  
Rohini M. Kalunge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Moving Average ◽  
Rainfall Variability ◽  
Least Square ◽  
Annual Rainfall ◽  
Series Data ◽  
Sen’S Slope ◽  
Increasing Trend ◽  
The Impact

The long-term behaviour of rainfall is necessary to study over space with different time series viz., annual, monthly and weekly as it is one of the most significant climatic variables. Rainfall trend is an important tool which assesses the impact of climate change and provides direction to cope up with its adverse effects on the agriculture. Several studies have been performed to establish the pattern of rainfall over various time periods for different areas that can be used for better agricultural planning, water supply management, etc. Consequently, the present report, entitled “Trend analysis of rainfall in Ahmednagar district of Maharashtra,” was carried out. 13 tahsils of the district of Ahmednagar were selected to carry out trend analysis. The daily rainfall data of 33 years (1980- 2012) of all stations has been processed out study the rainfall variability. The Mann Kendall (MK) Test, Sen’s slope method, moving average method and least square method were used for analysis. The statistical analysis of whole reference time series data highlighted that July and August month contributes highest amount of rainfall at all tahsils. Regarding trend in annual rainfall, these four methods showed increasing trend at most of the tahsils whereas a decreasing trend only at Shrigonda tahsil. For monthly trend analysis, Kopargaon, Newasa, Shevgaon and Shrirampur tahsils showed an increasing trend during July. During August and September month, most of the tahsils i.e. Kopargaon, Nagar, Parner and Sangamner showed increasing trends, whereas in June, only Shrigonda tahsil showed decreasing trend.

scholarly journals Innovative Trend Analysis of Annual and Seasonal Rainfall Variability in Amhara Regional State, Ethiopia

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 326 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Denghua Yan ◽  
Hao Wang ◽  
Tianling Qin ◽  
Abel Girma ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Seasonal Variability ◽  
Rainfall Variability ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Series Data ◽  
Analysis Method ◽  
Study Region ◽  
Regional State ◽  
Innovative Trend Analysis

This study investigated the annual and seasonal rainfall variability at five selected stations of Amhara Regional State, by using the innovative trend analysis method (ITAM), Mann-Kendall (MK) and Sen’s slope estimator test. The result showed that the trend of annual rainfall was increasing in Gondar (Z = 1.69), Motta (Z = 0.93), and Bahir Dar (Z = 0.07) stations. However, the trends in Dangla (Z = −0.37) and Adet (Z = −0.32) stations showed a decreasing trend. As far as monthly and seasonal variability of rainfall are concerned, all the stations exhibited sensitivity of change. The trend of rainfall in May, June, July, August, and September was increasing. However, the trend on the rest of other months showed a decreasing trend. The increase in rainfall during Kiremt season, along with the decrease in number of rainy days, leads to an increase of extreme rainfall events over the region during 1980–2016. The consistency in rainfall trends over the study region confirms the robustness of the change in trends. Innovative trend analysis method is very crucial method for detecting the trends in rainfall time series data due to its potential to present the results in graphical format as well. The findings of this paper could help researchers to understand the annual and seasonal variability of rainfall over the study region and become a foundation for further studies.

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

2010 ◽  
Vol 663 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Sonja Peters ◽  
Hans-Gerd Janssen ◽  
Gabriel Vivó-Truyols
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Novel Method ◽  
Analysis Of Time Series

scholarly journals Trend Analysis of Rainfall Time Series in Shanxi Province, Northern China (1957–2019)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2335
Author(s):  
Feng Gao ◽  
Yunpeng Wang ◽  
Xiaoling Chen ◽  
Wenfu Yang
Keyword(s):  
Time Series ◽  
Water Supply ◽  
Trend Analysis ◽  
Agricultural Production ◽  
Time Series Data ◽  
Shanxi Province ◽  
Series Data ◽  
Rainfall Time Series ◽  
Mk Test ◽  
Wutai Shan

Changes in rainfall play an important role in agricultural production, water supply and management, and social and economic development in arid and semi-arid regions. The objective of this study was to examine the trend of rainfall series from 18 meteorological stations for monthly, seasonal, and annual scales in Shanxi province over the period 1957–2019. The Mann–Kendall (MK) test, Spearman’s Rho (SR) test, and the Revised Mann–Kendall (RMK) test were used to identify the trends. Sen’s slope estimator (SSE) was used to estimate the magnitude of the rainfall trend. An autocorrelation function (ACF) plot was used to examine the autocorrelation coefficients at various lags in order to improve the trend analysis by the application of the RMK test. The results indicate remarkable differences with positive and negative trends (significant or non-significant) depending on stations. The largest number of stations showing decreasing trends occurred in March, with 10 out of 18 stations at the 10%, 5%, and 1% levels. Wutai Shan station has strong negative trends in January, March, April, November, and December at the level of 1%. In addition, Wutai Shan station also experienced a significant decreasing trend over four seasons at a significance level of 1% and 10%. On the annual scale, there was no significant trend detected by the three identification methods for most stations. MK and SR tests have similar power for detecting monotonic trends in rainfall time series data. Although similar results were obtained by the MK/SR and RMK tests in this study, in some cases, unreasonable trends may be provided by the RMK test. The findings of this study could benefit agricultural production activities, water supply and management, drought monitoring, and socioeconomic development in Shanxi province in the future.

Financial Time Series Data Mining

2011 ◽  
pp. 883-889
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Trend Analysis ◽  
Prediction Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Future Trend ◽  
Series Data ◽  
Time Series Data Mining ◽  
Financial Time

Movement of stocks in the financial market is a typical example of financial time series data. It is generally believed that past performance of a stock can indicate its future trend and so stock trend analysis is a popular activity in the financial community. In this chapter, we will explore the unique characteristics of financial time series data mining. Financial time series analysis came into being recently. Though the world’s first stock exchange was established in the 18th century, stock trend analysis began only in the late 20th century. According to Tay et al. (2003) analysis of financial time series has been formally addressed only since 1980s. It is believed that financial time series data can speak for itself. By analyzing the data, one can understand the volatility, seasonal effects, liquidity, and price response and hence predict the movement of a stock. For example, the continuous downward movement of the S&P index during a short period of time allows investors to anticipate that majority of stocks will go down in immediate future. On the other hand, a sharp increase in interest rate makes investors speculate that a decrease in overall bond price will occur. Such conclusions can only be drawn after a detailed analysis of the historic stock data. There are many charts and figures related to stock index movements, change of exchange rates, and variations of bond prices, which can be encountered everyday. An example of such a financial time series data is shown in Figure 1. It is generally believed that through data analysis, analysts can exploit the temporal dependencies both in the deterministic (regression) and the stochastic (error) components of a model and can come up with better prediction models for future stock prices (Congdon, 2003).

scholarly journals Application of Seasonal Autoregressive Moving Average Models to Analysis and Forecasting of Time Series Monthly Rainfall Patterns in Embu County, Kenya

2019 ◽  
pp. 1-15
Author(s):  
Tartisio Njoki Filder ◽  
Moses Mahugu Muraya ◽  
Robert Mathenge Mutwiri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Rainfall Variability ◽  
Model Fitting ◽  
Model Identification ◽  
Monthly Rainfall ◽  
Series Data ◽  
Estimation Model ◽  
Rainfall Variation

Rainfall is of critical importance for many people, particularly those whose livelihoods depend on rain-fed agriculture. Predicting the trend of rainfall is a difficult task, and statistical approaches such as time series analysis provide a means for predicting the patterns of rainfall. The models also offer the potential to improve areas such as increased food production, profitability, and improved food security policing. However, these forecasts and information systems may, in some instances, not be suitable for direct use by stakeholders in their decision-making. The objective of this study was to investigate rainfall variability and develop a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the monthly rainfall using time series data. Secondary monthly data from 1998 to 2017 for Embu County was collected from the Kenya Meteorological Department, Embu and recorded into an excel sheet. R-software was utilized to analyse data for descriptive statistics, rainfall variability, and model fitting. The coefficient of variation for annual and seasonal rainfall was calculated. The Box Jenkin's ARIMA modelling procedure (model identification, model estimation, model validation) was used to determine the best models for the data. The main study findings indicated the existence of annual variability of 34%, March-April-May rainfall variability of 44%, and October-November-December variability of 44%. A first-order differenced SARIMA (1, 1, 1) (0, 1, 2)12 model with an AIC score of 9.99356 was found suitable for predicting rainfall pattern in Embu, County. The study outcome revealed that Embu County experiences high seasonal and rainfall variation of rainfall, thus requires a reliable model for better prediction. 

scholarly journals Effect of Climate Variables on Yield of Major Crop in Samastipur District of Bihar: A time Series Analysis

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Meera Kumari
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Crop Yield ◽  
Minimum Temperature ◽  
Time Series Data ◽  
Annual Rainfall ◽  
Series Data ◽  
Climate Variables ◽  
Climatic Variable ◽  
Climate Variable

Climate change influences crop yield vis-a-vis crop production to a greater extent in Bihar. Climate change and its impacts are well recognizing today and it will affect both physical and biological system. Therefore, this study has been planned to assess the effect of climate variables on yield of major crops, adaptation measures undertaken in Samastipur district of Bihar. Secondary data on yield of maize and wheat crops were collected for the period from 1999-2019 to describe the effects of climate variable namely rainfall, maximum and minimum temperature on yield of maize and wheat. Analysis of time series data on climate variables indicated that annual rainfall was positively related to yields while maximum and minimum temperature had a negative but significant impact on maize and wheat yields. It actually revealed that other factors, such as; type of soil, soil fertility and method of farming may also be responsible for crop yield. Trend in cost as well as income of farmers indicated that income and cost of cultivation has no significant relationship with climate variable. On the basis of above observation it may be concluded that level of income of farmers changed due to change in the other factors rather than change in climatic variable over the period under study as cost of cultivation increases with increased in the price of input over the period but not due to change in climatic variable.

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