Rice is a staple in every Filipino home where it is eaten three times a day or sometimes more. Luzon is the top producer of rice for the past years among the other two island groups. Rice plays a critical role in food security. This is one of the importance of rice forecasting. This study explores the possibility of using spatial data and temporal data on forecasting the production of rice at the same time. A Spatio-temporal Forecasting model is used to forecast the quarterly harvest of each of the seven rice producing regions of Luzon. This enables the gathered data to be utilized and manipulated for rice production forecasting. The effect of spatial correlations on the prediction accuracy of spatial forecasting is explored. The study showed that Spatio-temporal forecasting model is better than the most commonly used ARIMA forecasting.

1.
ArcGIS: What is Temporal Data?
, http://desktop.arcgis.com
2.
M.J.B
Asutilla
,
M.
Sanhanda
, and
R. G.
Arcilla
,
Modelling and Forecasting Light Rail Transit Line 1 Patronage Using Seasonal Autoregressive Integrated Moving Average Method
(DLSU Research Congress
2016
).
3.
E.B
Barrios
and
R. F.
Lavado
,
Spatial-Temporal Stochastic Frontier Models
University of the Philippines Diliman.
4.
S.
Bergen
,
J.
Lindstrom
,
P.
Sampson
,
L.
Sheppard
, and
A.
Szpiro
,
SpatioTemporal: An R Package for Spatio-Temporal Modelling of Air-Pollution
,
34
.
5.
Bicol.da:
DA Funded Rice Processing Centers
(http://bicol.da.gov.ph/index.php/news/2637).
6.
A.
Brebels
,
T.A.
Janovsky
,
V.A.
Kamaev
,
N.L.
Shcherbakova
,
M.V.
Shcherbakova
, and
P.
Tyukov
,
A Survey of Forecast Error Measures
(
World AppliedSciences Journal
24
), pp.
171
176
.
7.
Carleton College
: “
What is Special about Spatial Analysis
(https://apps.carleton.edu)
8.
T.
Cheng
,
J.
Wang
and
X.
Lib
,
Space-Time Series Forecasting By Artificial Neural Networks
(
Proceedings of SPIE - The International Society for Optical Engineering
)
2008
.
9.
T.
Cheng
and
J.
Wang
, “
Applications Of Spatio-Temporal Data Mining And Knowledge Discovery (STDMKD) For Forest Fire Prevention
”.
10.
Columbia University Mailman School of Public Health, “Spatiotemporal Analysis
” (www.mailman.columbia.edu).
11.
CountryStat
, “
Regional Profile: Bicol
” (http://countrystat.psa.gov.ph/?cont=16r=5).
12.
CountryStat
: “
Regional Profile: Cagayan Valley
” (countrystat.psa.gov.ph/?cont=16r=2).
13.
CountryStat
: “
Regional Profile: CALABARZON
” (http://countrystat.psa.gov.ph/?cont=16r=4).
14.
CountryStat
: “
Regional Profile: CAR
” (http://countrystat.psa.gov.ph/?cont=16r=14).
15.
CountryStat
: “
Regional Profile: Central Luzon
” (http://countrystat.psa.gov.ph/?cont=16r=3).
16.
CountryStat
: “
Regional Profile: Ilocos Region
” (countrystat.psa.gov.ph/?cont=16r=1).
17.
CountryStat
: “
Regional Profile: MIMAROPA
” (http://countrystat.psa.gov.ph/?cont=16r=17).
18.
CountrySTAT
: “
The CountrySTAT Philippines
” (http://countrystat.psa.gov.ph/?cont=1).
19.
A.C. dela
Cruz
,
J.A.
Lubrica
,
B.V.D.C.
Punzalan
and
M. C.
Martin
,
Forecasting dengue incidence in the National Capital Region, Philippines: using time series analysis with climate variables as predictors
(
Acta Manilana
),
2012
, pp.
19
26
.
20.
DOST Agricultural Machinery Information Network: “Problem, Issues, and Constraints
” (www.pcaarrd.dost.gov.ph).
21.
Duke.edu: “Introduction to ARIMA
” (https://people.duke.edu/ rnau/411arim.htm).
22.
ESRI: “GIS Dictionary
” (http://support.esri.com).
23.
M.M.
Fischer
, and
J.
Wang
,
Spatial Data Analysis : Models, Methods, Techniques
(
Springer-Verlag Berlin Heidelberg
),
2011
,
70
.
24.
M.A.
Hamjah
,
Rice Production Forecasting in Bangladesh: An Application Of Box-Jenkins ARIMA Model
(
Mathematical Theory and Modeling
),
2014
, pp.
1
11
.
25.
O.Z.
Landagan
and
E. B.
Barrios
,
An estimation procedure for a spatialtemporal model
(
Statistics and Probability Letters
),
2007
, pp.
401
406
.
26.
Z.
Li
,
and M.
Dunham
, “
STIFF: A Forecasting Framework for Spatio-Temporal Data
27.
A.
Lucero
,
N.
Koide
and et.al.,
Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts
(
Journal of Applied Meteorology and Climatology
),
2012
, pp.
552
569
.
28.
A.D.
Nalica
,
Spatial-Temporal Modeling of Growth in Rice Production in the Philippines
(
The Philippine Statistician
),
2010
, pp.
15
25
.
29.
Nature: “Time Series
” (http://www.nature.com/subjects/time-series).
30.
OECD: “Time Series
” (https://stats.oecd.org).
31.
A.
Paz-Nalica
and
E. B.
Barrios
,
Approaches in Forecasting Cereals Production
(
The Philippine Statistician
),
2008
, pp.
85
102
.
32.
PSA: “A Review of the Agriculture Sector in CALABARZON
” (https://psa.gov.ph/content/review-agriculture-sector-calabarzon).
33.
PSA: “A Review of the Agriculture Sector in Cagayan Valle
” (https://psa.gov.ph/content/review-agriculture-sector-cagayan-valley).
34.
PSA: “A Review of the Agriculture Sector in Central Luzon
” (https://psa.gov.ph/content/review-agriculture-sector-central-luzon).
35.
PSA: “A Review of the Agriculture Sector in Ilocos Region
” (https://psa.gov.ph/content/review-agriculture-sector-ilocos-region).
36.
PSA: “A Review of the Agriculture Sector in MIMAROPA
” (https://psa.gov.ph/content/review-agriculture-sector-mimaropa).
38.
Ricepedia: “Philippines Basic Statistics
” (www.ricepedia.org)
39.
A.
Roel
and
R.
Plant
,
Spatiotemporal Analysis of Rice Yield Variability in California
,
15
pp.
40.
P.
Saengseedam
and
N.
Kantanantha
,
Spatial Time Series Models for Rice and Cassava Yields Based On Bayesian Linear Mixed Models
(
International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering
)
2014
, pp.
1063
1068
.
41.
SFSU: “Time Series Analysis
” (userwww.sfsu.edu).
42.
T.E.
Smith
, “
Spatial Weight Matrix
”.
43.
A.M.
Tamayo
, “
Forecast Model of the Gross Domestic Product: An Application of Box-Jenkins Methodology
”.
44.
45.
UNESCO: “Rice Terraces
” (http://whc.unesco.org/en/list/722).
46.
J.D.
Urrutia
,
J.L. B.
Diaz
and
F.L. T.
Mingo
,
Forecasting the Quarterly Production of Rice and Corn in the Philippines: A Time Series Analysis
(
Journal of Physics: Conf. Series
820
),
2017
.
47.
J.D.
Urrutia
,
M.L. T.
Olfindo
and
R.
Tampis
.
Modelling and Forecasting the Exchange Rate of the Philippines: A Time Series Analysis
(
American Research Thoughts
),
2015
, pp.
1880
1937
.
48.
S.J.
Villejo
,
Modelling Rice Yield in the Philippines using Dynamic Spatio-Temporal Models
(
The Philippine Statistician
),
2015
, pp.
43
58
.
49.
J.
Wang
and
T.
Cheng
,
Integrated Spatio-temporal Data Mining for Forest Fire Prediction
(
Transactions in GIS
),
2008
,
12
(
5
):
591611
.
50.
C.
Wikle
, “
Statistics for Spatio-Temporal Data Introduction, Visualization, Descriptive Methods
”,
2012
.
This content is only available via PDF.