In a study of 81 species of Australian plants Hudson & Keatley (2021) investigated whether the day of occurrence of a species first flowering (first flowering day, FFD, a number between 1 -365) is impacted by climate factors such as rainfall (rain), temperature (temp), radiation level (rad), and relative humidity (RH). The study by Hudson & Keatley essentially explores the influence of long-term climate on the FFD of 81 species of plants from 1984 to 2014.

Your data focuses on one species (of the 81) and contains 5 time series, the FFD time series of the given plant species and the contemporaneous yearly averaged climate variables measured from 1984 - 2014 (31 years). All series are available here inFFD .csvDownload FFD .csv

Your task is to model FFD and forecast FFD. Single climate predictors (univariate models) are to be tested. Your task is to give best FFD 4 years ahead forecasts for the FFD series. Point forecasts and confidence intervals are required for the forecasts with appropriate graphs.

Provide the point forecasts and confidence intervals and corresponding plot for the most optimal model foreach method used(DLM, ARDL, polyck, koyck, dynamic, exponential smoothing and state-space model).

Use MASE() function from the dLagM package to compute MASE for time series regression methods for model comparisons. Choice of optimal models within a specific method can be assessed from values of R squared, AIC, BIC, MASE etc (as is appropriate to the method).

Hints:

• Perform univariate analyse of predictors.
• Run models with and without intercept.
• Interpret the lag effects - note positive lag coefficient means that flowering is later and negative ones earlier flowering (lower FFD).
• Intercept denotes the intercept mean FFD. You are to analyse one given species (on Canvas).
• Plots are essential ACF PACF, forecasts etc.
• Percentiles method for relevant covariates for the forecasts can be used.

I JUST NEED THE R CODE FOR THIS IN RMD FORMAT.

THE FIRST PART IN THE BELOW LINK CAN HELP YOU SOLVE THIS QUESTION

https://rpubs.com/AnnaKrinochkina/537658

BELOW IS THE CSV FILE OR THE INPUT DATA.

FDD.csv

Year	Temperature	Rainfall	Radiation	RelHumidity	FFD
1984	9.371585	2.489344	14.87158	93.9265	217
1985	9.656164	2.47589	14.68493	94.93589	186
1986	9.273973	2.42137	14.51507	94.09507	233
1987	9.219178	2.319726	14.67397	94.49699	222
1988	10.20219	2.465301	14.74863	94.08142	214
1989	9.441096	2.73589	14.78356	96.08685	237
1990	9.943836	2.39863	14.67671	93.77918	213
1991	9.690411	2.635616	14.41096	93.15562	206
1992	9.691257	2.795902	13.39617	94.09863	188
1993	9.947945	2.87863	14.26575	94.91973	234
1994	9.316438	1.974795	14.52329	93.26932	264
1995	9.164384	2.843288	13.90411	94.45863	196
1996	8.967213	2.814754	14.3306	94.6	229
1997	9.038356	1.403014	14.77534	93.74685	212
1998	8.934247	2.289041	14.6	94.60822	244
1999	9.547945	2.126301	14.6137	96.22603	178
2000	9.680328	2.471858	14.65574	95.65738	154
2001	9.561644	2.227945	14.14521	94.70712	207
2002	9.389041	1.74	14.63836	93.53233	182
2003	9.210959	2.270411	15.11233	94.47096	218
2004	9.300546	2.620492	14.64481	95.01421	192
2005	9.623288	2.28411	15.09315	94.30356	199
2006	8.715068	1.78137	15.41096	94.84493	200
2007	9.80137	2.191233	15.19452	94.11068	225
2008	9.034153	1.743169	14.80328	94.39508	216
2009	9.457534	2.03863	15.12877	94.63096	197
2010	9.765753	2.777808	14.29315	96.05205	230
2011	9.826027	2.886301	14.01096	95.70603	204
2012	9.76776	2.599454	14.4071	94.90519	233
2013	10.09726	2.540274	14.43014	93.83479	174
2014	10.24725	2.239286	14.60165	94.21016	189