An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique
Saved in:
Published in | Applied soft computing Vol. 149; p. 110939 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
01.12.2023
|
Online Access | Get full text |
Cover
Loading…
ArticleNumber | 110939 |
---|---|
Author | Gurung, Bishal Ray, Soumik Lama, Achal Sankar Das, Soumitra Mishra, Pradeep Biswas, Tufleuddin |
Author_xml | – sequence: 1 givenname: Soumik surname: Ray fullname: Ray, Soumik – sequence: 2 givenname: Achal orcidid: 0000-0002-5376-3760 surname: Lama fullname: Lama, Achal – sequence: 3 givenname: Pradeep orcidid: 0000-0003-4430-886X surname: Mishra fullname: Mishra, Pradeep – sequence: 4 givenname: Tufleuddin orcidid: 0000-0001-7978-5098 surname: Biswas fullname: Biswas, Tufleuddin – sequence: 5 givenname: Soumitra surname: Sankar Das fullname: Sankar Das, Soumitra – sequence: 6 givenname: Bishal surname: Gurung fullname: Gurung, Bishal |
BookMark | eNp9kMtOwzAQRb0oEi3wA6z8Awl2YjvxMqp4VGqFBGVtOfakdZXGYLsg_p5EZcUCzWI291zNnAWaDX4AhG4pySmh4u6Q6-hNXpCizCklspQzNKdc1BmTTFyiRYwHMgZlUc9R2wy4eVltmmz9ut3go7fQ484H_B7AOpPcsMOfvtfJ9YD1Ljhz6tMp6H4MOAM4QnAQ8ZdLexz0YP1xoiEmnMDsB_dxgmt00ek-ws3vvkJvD_fb5VO2fn5cLZt1ZkpCUiYLycu6Elxaw2oiCt6WnBHJpKxaQaWo-DiyKytWWy5BV1DoVtfQWss45eUVqs-9JvgYA3TKuDQe7ocUtOsVJWryow5q8qMmP-rsZ0SLP-j43VGH7_-gH7Gtbps |
CitedBy_id | crossref_primary_10_1007_s00170_024_14105_6 crossref_primary_10_1007_s11540_024_09736_x crossref_primary_10_1016_j_oceaneng_2024_117648 crossref_primary_10_1109_ACCESS_2024_3409822 crossref_primary_10_1007_s11540_024_09717_0 crossref_primary_10_1016_j_jclepro_2023_140303 crossref_primary_10_1016_j_measurement_2024_116126 crossref_primary_10_1002_for_3246 crossref_primary_10_1080_15140326_2025_2454081 crossref_primary_10_1007_s40890_024_00216_y crossref_primary_10_1016_j_jclepro_2025_145079 crossref_primary_10_1016_j_scitotenv_2024_174271 crossref_primary_10_1016_j_simpa_2024_100650 crossref_primary_10_3390_pr12122700 crossref_primary_10_1109_TITS_2024_3510678 crossref_primary_10_3390_jmse12122344 crossref_primary_10_3390_horticulturae10040347 crossref_primary_10_1007_s11540_024_09823_z crossref_primary_10_1016_j_apenergy_2025_125459 crossref_primary_10_1063_5_0213366 crossref_primary_10_18615_anadolu_1385394 crossref_primary_10_4108_ew_5787 crossref_primary_10_1016_j_ins_2025_121995 crossref_primary_10_1109_ACCESS_2024_3439998 crossref_primary_10_1109_ACCESS_2024_3453664 crossref_primary_10_1016_j_jrurstud_2024_103491 crossref_primary_10_1007_s11540_023_09680_2 crossref_primary_10_3390_rs16224136 crossref_primary_10_1007_s10586_024_04909_2 crossref_primary_10_1007_s12665_024_11481_w crossref_primary_10_1016_j_asoc_2024_112423 crossref_primary_10_21015_vtm_v12i1_1894 crossref_primary_10_3390_agriculture15050469 crossref_primary_10_1088_1402_4896_ad88ba crossref_primary_10_3390_forecast6040046 crossref_primary_10_1016_j_eswa_2024_124195 crossref_primary_10_1016_j_energy_2025_134470 crossref_primary_10_1016_j_energy_2025_134492 crossref_primary_10_1016_j_clcb_2024_100132 crossref_primary_10_3390_math12152308 crossref_primary_10_1016_j_eswa_2023_123088 crossref_primary_10_3390_sym16091248 |
Cites_doi | 10.4236/jmf.2017.71007 10.1109/IJCNN.2010.5596890 10.1080/07350015.1995.10524599 10.1063/1.1477036 10.2307/1912773 10.1016/j.eswa.2018.03.002 10.1093/biomet/65.2.297 10.3390/rs10081217 10.1214/aos/1176344136 10.1016/j.physa.2019.04.043 10.1016/0304-4076(86)90063-1 10.5958/0974-0279.2015.00005.1 10.1016/j.energy.2021.121543 10.1007/s41748-021-00205-w 10.1007/s12572-021-00312-x 10.1162/neco.1997.9.8.1735 10.1080/02664760701231633 10.1016/j.physa.2020.124907 10.1080/00401706.1969.10490657 10.1142/6578 10.1016/j.energy.2022.126125 10.3390/sym11070912 10.1109/TPAMI.2005.159 10.1093/biomet/75.2.335 10.1186/1471-2105-15-276 10.1080/00401706.1972.10488930 10.5958/2322-0430.2016.00199.2 10.1016/j.physa.2019.123532 10.1016/j.resourpol.2022.103249 10.1109/TAC.1974.1100705 10.1016/S0893-6080(05)80125-X 10.3390/su12010393 10.5958/0976-4666.2014.00009.6 10.3390/s20247211 10.1080/07474938.2015.1114313 10.3390/app10228169 10.3390/electronics10202518 |
ContentType | Journal Article |
DBID | AAYXX CITATION |
DOI | 10.1016/j.asoc.2023.110939 |
DatabaseName | CrossRef |
DatabaseTitle | CrossRef |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Computer Science |
ExternalDocumentID | 10_1016_j_asoc_2023_110939 |
GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO AAYXX ABBOA ABFNM ABFRF ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AFXIZ AGCQF AGHFR AGQPQ AGRNS AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV CITATION CS3 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SSH SST SSV SSZ T5K UHS UNMZH ~G- |
ID | FETCH-LOGICAL-c300t-9295387659dc480625b354094997b6196757579f3748d59ea7e2aba8ebdd45153 |
ISSN | 1568-4946 |
IngestDate | Thu Apr 24 23:09:42 EDT 2025 Tue Jul 01 01:50:23 EDT 2025 |
IsPeerReviewed | true |
IsScholarly | true |
Language | English |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c300t-9295387659dc480625b354094997b6196757579f3748d59ea7e2aba8ebdd45153 |
ORCID | 0000-0002-5376-3760 0000-0001-7978-5098 0000-0003-4430-886X |
ParticipantIDs | crossref_citationtrail_10_1016_j_asoc_2023_110939 crossref_primary_10_1016_j_asoc_2023_110939 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2023-12-00 |
PublicationDateYYYYMMDD | 2023-12-01 |
PublicationDate_xml | – month: 12 year: 2023 text: 2023-12-00 |
PublicationDecade | 2020 |
PublicationTitle | Applied soft computing |
PublicationYear | 2023 |
References | Hochreiter (10.1016/j.asoc.2023.110939_bib13) 1997; 9 Dickey (10.1016/j.asoc.2023.110939_bib43) 1979; 74 Li (10.1016/j.asoc.2023.110939_bib29) 2021; 237 Funahashi (10.1016/j.asoc.2023.110939_bib12) 1993; 6 Cortez (10.1016/j.asoc.2023.110939_bib24) 2010 Om (10.1016/j.asoc.2023.110939_bib16) 2020; 10 Diebold (10.1016/j.asoc.2023.110939_bib50) 1995; 13 Tsangari (10.1016/j.asoc.2023.110939_bib20) 2007; 34 Grubbs (10.1016/j.asoc.2023.110939_bib41) 1969; 11 Engle (10.1016/j.asoc.2023.110939_bib2) 1982; 50 Kulshreshtha (10.1016/j.asoc.2023.110939_bib31) 2020; 13 Gao (10.1016/j.asoc.2023.110939_bib28) 2021 Shiferaw (10.1016/j.asoc.2023.110939_bib9) 2019; 526 10.1016/j.asoc.2023.110939_bib49 Epaphra (10.1016/j.asoc.2023.110939_bib4) 2017; 7 Lama (10.1016/j.asoc.2023.110939_bib1) 2015; 28 Yi (10.1016/j.asoc.2023.110939_bib18) 2019; 11 Kane (10.1016/j.asoc.2023.110939_bib26) 2014; 15 Phurun (10.1016/j.asoc.2023.110939_bib30) 2020; 3 Hu (10.1016/j.asoc.2023.110939_bib35) 2020; 557 Schwarz (10.1016/j.asoc.2023.110939_bib48) 1978; 6 Ljung (10.1016/j.asoc.2023.110939_bib45) 1978; 65 Bhardwaj (10.1016/j.asoc.2023.110939_bib7) 2014; 59 Yuan (10.1016/j.asoc.2023.110939_bib8) 2020; 12 Zeng (10.1016/j.asoc.2023.110939_bib34) 2023; 263 Phillips (10.1016/j.asoc.2023.110939_bib44) 1988; 75 Han (10.1016/j.asoc.2023.110939_bib23) 2016; 36 Bollerslev (10.1016/j.asoc.2023.110939_bib3) 1986; 31 Yoo (10.1016/j.asoc.2023.110939_bib17) 2020; 10 Scargle (10.1016/j.asoc.2023.110939_bib22) 2002; 617 Lin (10.1016/j.asoc.2023.110939_bib10) 2020; 543 Nguyen (10.1016/j.asoc.2023.110939_bib52) 2014 Dritsaki (10.1016/j.asoc.2023.110939_bib6) 2018; 8 Markiewicz (10.1016/j.asoc.2023.110939_bib27) 2021; 13 Surakhi (10.1016/j.asoc.2023.110939_bib25) 2021; 10 Cheikh (10.1016/j.asoc.2023.110939_bib5) 2019; 35 Goutte (10.1016/j.asoc.2023.110939_bib21) 1997 Ray (10.1016/j.asoc.2023.110939_bib37) 2021; 5 Mishra (10.1016/j.asoc.2023.110939_bib47) 2021 Namini (10.1016/j.asoc.2023.110939_bib14) 2018 Zhou (10.1016/j.asoc.2023.110939_bib19) 2020; 20 Kim (10.1016/j.asoc.2023.110939_bib32) 2018; 103 Ray (10.1016/j.asoc.2023.110939_bib38) 2016; 12 Majid (10.1016/j.asoc.2023.110939_bib11) 2018; 63 Ndikumana (10.1016/j.asoc.2023.110939_bib40) 2018; 10 10.1016/j.asoc.2023.110939_bib39 Peng (10.1016/j.asoc.2023.110939_bib51) 2005; 27 10.1016/j.asoc.2023.110939_bib36 Akaike (10.1016/j.asoc.2023.110939_bib46) 1974; 19 Kurumarani (10.1016/j.asoc.2023.110939_bib15) 2020; 2 Stefansky (10.1016/j.asoc.2023.110939_bib42) 1972; 14 Srivastava (10.1016/j.asoc.2023.110939_bib33) 2023; 80 |
References_xml | – volume: 7 start-page: 121 year: 2017 ident: 10.1016/j.asoc.2023.110939_bib4 article-title: Modeling exchange rate volatility: Application of GARCH and EGARCH models publication-title: J. Mathe Financ. doi: 10.4236/jmf.2017.71007 – volume: 10 start-page: 16 issue: 1 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib16 article-title: Modelling email traffic workload with RNN and LSTM models publication-title: Hum. -Centr Comput. Inf. Sci. – year: 2010 ident: 10.1016/j.asoc.2023.110939_bib24 article-title: Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines publication-title: 2010 Int. Jt. Conf. Neural Netw. (IJCNN) doi: 10.1109/IJCNN.2010.5596890 – volume: 13 start-page: 253 year: 1995 ident: 10.1016/j.asoc.2023.110939_bib50 article-title: Comparing predicting accuracy publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.1995.10524599 – volume: 617 start-page: 23 year: 2002 ident: 10.1016/j.asoc.2023.110939_bib22 article-title: Bayesian estimation of time series lags and structure publication-title: AIP Conf. Proc. doi: 10.1063/1.1477036 – volume: 50 start-page: 987 year: 1982 ident: 10.1016/j.asoc.2023.110939_bib2 article-title: Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation publication-title: Econometrica doi: 10.2307/1912773 – volume: 3 start-page: 35 issue: 1 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib30 article-title: Shallot Price Forecasting Model Using Hybrid ARIMA-LSTM Model publication-title: Data Sci. Eng. (DSE) Rec. – volume: 103 start-page: 25 year: 2018 ident: 10.1016/j.asoc.2023.110939_bib32 article-title: 'Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models' publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.03.002 – volume: 2 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib15 article-title: Time series forecasting of agricultural product prices based on recurrent neural networks and its evaluation method publication-title: SN Appl. Sci. – volume: 65 start-page: 297 year: 1978 ident: 10.1016/j.asoc.2023.110939_bib45 article-title: On a measure of lack of fit in time series models publication-title: Biometrika doi: 10.1093/biomet/65.2.297 – volume: 10 start-page: 1217 issue: 8 year: 2018 ident: 10.1016/j.asoc.2023.110939_bib40 article-title: Deep recurrent neural network for agricultural classification using multitemporal SAR sentinel-1 for camargue, France publication-title: Remote Sens. doi: 10.3390/rs10081217 – year: 1997 ident: 10.1016/j.asoc.2023.110939_bib21 article-title: Lag space estimation in time series modelling, 1997 IEEE International Conference on Acoustics publication-title: Speech, Signal Process. – volume: 6 start-page: 461 year: 1978 ident: 10.1016/j.asoc.2023.110939_bib48 article-title: Estimating the dimension of a model publication-title: Ann. Stat. doi: 10.1214/aos/1176344136 – volume: 526 year: 2019 ident: 10.1016/j.asoc.2023.110939_bib9 article-title: Time-varying correlation between agricultural commodity and energy price dynamics with bayesian multivariate DCC-GARCH models publication-title: Phys. A: Stat. Mech. its Appl. doi: 10.1016/j.physa.2019.04.043 – volume: 31 start-page: 307 year: 1986 ident: 10.1016/j.asoc.2023.110939_bib3 article-title: Generalized autoregressive conditional Heteroskedasticity publication-title: J. Econ. doi: 10.1016/0304-4076(86)90063-1 – volume: 28 start-page: 73 year: 2015 ident: 10.1016/j.asoc.2023.110939_bib1 article-title: Modelling and forecasting of price volatility: An application of GARCH and EGARCH models publication-title: Agric. Econ. Res. Rev. doi: 10.5958/0974-0279.2015.00005.1 – volume: 237 year: 2021 ident: 10.1016/j.asoc.2023.110939_bib29 article-title: Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling publication-title: Energy doi: 10.1016/j.energy.2021.121543 – volume: 5 start-page: 531 year: 2021 ident: 10.1016/j.asoc.2023.110939_bib37 article-title: Time series SARIMA modelling and forecasting of monthly rainfall and temperature in the South Asian countries publication-title: Earth Syst. Environ. doi: 10.1007/s41748-021-00205-w – volume: 13 start-page: 248 year: 2021 ident: 10.1016/j.asoc.2023.110939_bib27 article-title: Time series forecasting: problem of heavy-tailed distributed noise publication-title: Int J. Adv. Eng. Sci. Appl. Math. doi: 10.1007/s12572-021-00312-x – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.asoc.2023.110939_bib13 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – start-page: 1 year: 2021 ident: 10.1016/j.asoc.2023.110939_bib28 article-title: A Hybrid Model Integrating LSTM and Garch for Bitcoin Price Prediction publication-title: 2021 IEEE 31st Int. Workshop Mach. Learn. Signal Process. (MLSP) – ident: 10.1016/j.asoc.2023.110939_bib49 – volume: 34 start-page: 403 year: 2007 ident: 10.1016/j.asoc.2023.110939_bib20 article-title: An alternative methodology for combining different forecast models publication-title: J. Appl. Stat. doi: 10.1080/02664760701231633 – volume: 557 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib35 article-title: A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction publication-title: Phys. A: Stat. Mech. its Appl. doi: 10.1016/j.physa.2020.124907 – volume: 11 start-page: 1 year: 1969 ident: 10.1016/j.asoc.2023.110939_bib41 article-title: Procedures for detecting outlying observation in samples publication-title: Technometrics doi: 10.1080/00401706.1969.10490657 – ident: 10.1016/j.asoc.2023.110939_bib39 doi: 10.1142/6578 – volume: 263 year: 2023 ident: 10.1016/j.asoc.2023.110939_bib34 article-title: Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM publication-title: Energy doi: 10.1016/j.energy.2022.126125 – volume: 8 start-page: 14 issue: 3 year: 2018 ident: 10.1016/j.asoc.2023.110939_bib6 article-title: The performance of hybrid ARIMA-GARCH modeling and forecasting oil price publication-title: Int. J. Energy Econ. Policy – volume: 11 start-page: 1 year: 2019 ident: 10.1016/j.asoc.2023.110939_bib18 article-title: An enhanced algorithm of RNN using trend in time series publication-title: Symmetry doi: 10.3390/sym11070912 – volume: 27 start-page: 1226 year: 2005 ident: 10.1016/j.asoc.2023.110939_bib51 article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.159 – year: 2021 ident: 10.1016/j.asoc.2023.110939_bib47 article-title: Modeling and forecasting of milk production in the SAARC countries and China publication-title: Model. earth Syst. Environ. – volume: 63 start-page: 815 year: 2018 ident: 10.1016/j.asoc.2023.110939_bib11 article-title: Advances in statistical forecasting methods: An overview publication-title: Econ. Aff. – year: 2018 ident: 10.1016/j.asoc.2023.110939_bib14 article-title: A comparison of ARIMA and LSTM in forecasting time series publication-title: Pap. 17th IEEE Int. Conf. Mach. Learn. Appl. – volume: 75 start-page: 335 year: 1988 ident: 10.1016/j.asoc.2023.110939_bib44 article-title: Testing for a unit root in time series regression publication-title: Biometrika doi: 10.1093/biomet/75.2.335 – volume: 35 start-page: 1 year: 2019 ident: 10.1016/j.asoc.2023.110939_bib5 article-title: Asymmetric volatility in crytocurrency markets: New evidence from smooth transition GARCH models publication-title: Financ. Res. Lett. – volume: 15 year: 2014 ident: 10.1016/j.asoc.2023.110939_bib26 article-title: Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks publication-title: BMC Bioinforma. doi: 10.1186/1471-2105-15-276 – volume: 14 start-page: 469 year: 1972 ident: 10.1016/j.asoc.2023.110939_bib42 article-title: Rejecting outliers in factorial design publication-title: Technometrics doi: 10.1080/00401706.1972.10488930 – volume: 12 start-page: 739 year: 2016 ident: 10.1016/j.asoc.2023.110939_bib38 article-title: Statistical modeling and forecasting of food grain in effects on public distribution system: an application of ARIMA model publication-title: Indian J. Econ. Dev. doi: 10.5958/2322-0430.2016.00199.2 – volume: 543 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib10 article-title: Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model publication-title: Phys. A: Stat. Mech. its Appl. doi: 10.1016/j.physa.2019.123532 – start-page: 512 year: 2014 ident: 10.1016/j.asoc.2023.110939_bib52 article-title: Effective global approaches for mutual information based feature selection publication-title: KDD’14 – volume: 80 year: 2023 ident: 10.1016/j.asoc.2023.110939_bib33 article-title: What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning publication-title: Resour. Policy doi: 10.1016/j.resourpol.2022.103249 – volume: 19 start-page: 716 year: 1974 ident: 10.1016/j.asoc.2023.110939_bib46 article-title: A new look at the statistical model identification publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.1974.1100705 – volume: 6 start-page: 801 year: 1993 ident: 10.1016/j.asoc.2023.110939_bib12 article-title: Approximation of Dynamical systems by continuous time recurrent neural networks publication-title: Neural Netw. doi: 10.1016/S0893-6080(05)80125-X – ident: 10.1016/j.asoc.2023.110939_bib36 – volume: 12 start-page: 17 issue: 1 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib8 article-title: Modeling Co-Movement among different agricultural commodity markets: A copula-GARCH approach publication-title: Sustainability doi: 10.3390/su12010393 – volume: 59 start-page: 415 year: 2014 ident: 10.1016/j.asoc.2023.110939_bib7 article-title: An empirical investigation of arima and garch models in agricultural price forecasting publication-title: Econ. Aff. doi: 10.5958/0976-4666.2014.00009.6 – volume: 20 start-page: 1 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib19 article-title: Time series forecasting and classification models based on recurrent with attention mechanism and generative adversarial networks publication-title: Sensors doi: 10.3390/s20247211 – volume: 36 start-page: 225 year: 2016 ident: 10.1016/j.asoc.2023.110939_bib23 article-title: Lag length selection using panel autoregression publication-title: Econom. Rev. doi: 10.1080/07474938.2015.1114313 – volume: 74 start-page: 427 year: 1979 ident: 10.1016/j.asoc.2023.110939_bib43 article-title: Distribution of the estimators for autoregressive time series with a unit root publication-title: J. Am. Stat. Assoc. – volume: 13 issue: 4 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib31 article-title: An ARIMA-LSTM hybrid model for stock market prediction using live data publication-title: J. Eng. Sci. Technol. Rev. – volume: 10 start-page: 1 year: 2020 ident: 10.1016/j.asoc.2023.110939_bib17 article-title: Time series forecasting of agricultural products sales volumes based on seasonal long short-term memory publication-title: Appl. Sci. doi: 10.3390/app10228169 – volume: 10 start-page: 2518 year: 2021 ident: 10.1016/j.asoc.2023.110939_bib25 article-title: Time-lag selection for time-series forecasting using neural network and heuristic algorithm publication-title: Electronics doi: 10.3390/electronics10202518 |
SSID | ssj0016928 |
Score | 2.61654 |
SourceID | crossref |
SourceType | Enrichment Source Index Database |
StartPage | 110939 |
Title | An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique |
Volume | 149 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Ja-MwFBZdLr20M11oOzNFh96MghV5PZoyQ6ckpbQp9GYkS-lC44TEYWB-_TwtXrpMaUvABCML4_fxNr33PYSOIQLgLJUh8dNxSAKRjAmnMSWcRTETkiphjguG59HpdXB2E960JUGmu6QSveLvq30ln5Eq3AO56i7ZD0i22RRuwH-QL1xBwnB9l4yz0ssufw8zMrgaDe1QG1M2OJvr4xdT0AzaBxY_Ko_fzluajZmmEvL0OyrX3gYmS04n-mkwE17D7Nr1XWuHdQGa25SiL6va7pmDIls1Nl1O7pvunwGf2LxtccfbAsT7xZ2ZbqTJkqRSszZRv_hj-8tGSz1TWEpHC-6yEn3WqfBwijRKSJC69GKtaS07qdOVmuvUEhm9UOM2o_DQ44DQnt6-1y5-ypn9zJY1FYZ18dpDrvfI9R653WMVrfchpACduJ6dXA4umjOnKDWTeJs3dy1Wthrw-Zt03JiOPzL6gjZdIIEzi4qvaEWV22irHtKBnc7eQSIrcQsSbECCQcy4BQmuQYK7IMEGJNiCBGuQYAsSbEGCG5DsoutfP0cnp8QN1iAF8_2KgEsMdi6OwlQWQeJDCCx0-k_zFMUCImoIIuGXjjU1kQxTxWPV54InSkgZgAPM9tBaOS3VPsKcj30qVMxYwAJFRQJGgirGZFBQJQU9QLT-TnnhWOf18JPH_P8SOkBe88zMcq68sfrwQ6u_oY0Wrt_RWjVfqh_gVlbiyOHhH92KeGI |
linkProvider | Elsevier |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+ARIMA-LSTM+model+for+predicting+volatile+agricultural+price+series+with+random+forest+technique&rft.jtitle=Applied+soft+computing&rft.au=Ray%2C+Soumik&rft.au=Lama%2C+Achal&rft.au=Mishra%2C+Pradeep&rft.au=Biswas%2C+Tufleuddin&rft.date=2023-12-01&rft.issn=1568-4946&rft.volume=149&rft.spage=110939&rft_id=info:doi/10.1016%2Fj.asoc.2023.110939&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_asoc_2023_110939 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |