The Security Service Edge (SSE) market, valued at $729.4 million in 2025, is poised for steady growth, driven by the increasing adoption of cloud-based applications and the rising concerns around data security in a distributed workforce environment. The market's Compound Annual Growth Rate (CAGR) of 2.6% indicates a consistent expansion, projected to continue through 2033. Key drivers include the need for enhanced security posture management, improved visibility into cloud traffic, and simplified security management for increasingly complex IT infrastructures. The shift towards remote work models, the proliferation of SaaS applications, and the growing adoption of 5G networks are further accelerating the market's expansion. Segmentation reveals strong demand across various sectors, including IT, BFSI (Banking, Financial Services, and Insurance), Manufacturing, Transportation, and Entertainment, with cloud-based solutions dominating the market share due to their scalability and flexibility. Competitive landscape is robust, featuring established players like Zscaler, Palo Alto Networks, and Cisco, alongside emerging innovators, fueling innovation and competition. Growth within the SSE market will be influenced by several factors. Geographic expansion, particularly in regions like Asia Pacific, fueled by increasing digitalization and government investments in cybersecurity infrastructure, will contribute significantly to market growth. Furthermore, ongoing innovation in areas like Zero Trust Network Access (ZTNA) and Secure Access Service Edge (SASE) technologies, which are increasingly integrated within SSE offerings, will continue to attract new customers and drive adoption. However, challenges remain, including the complexities of integration with existing security systems, potential skill gaps in managing SSE solutions, and concerns regarding cost optimization, particularly for smaller enterprises. Nevertheless, the long-term prospects for SSE remain positive, indicating a substantial market opportunity for both established and emerging players.

The Security Service Edge (SSE) market is rapidly expanding, driven by the increasing adoption of cloud services and the rising threats of cyberattacks. The global SSE market is estimated to grow from $5.1 billion in 2023 to $22.4 billion by 2028, at a CAGR of 34.8%. North America is currently the largest regional market for SSE, accounting for over 40% of the global market share. However, the Asia-Pacific region is expected to experience the highest growth rate over the forecast period, due to the increasing adoption of cloud services in this region. The major players in the SSE market include Zscaler, Palo Alto Networks, Cisco, Cato Networks, Netskope, Proofpoint, Barracuda Networks, Menlo Security, Cloudflare, Forcepoint, Skyhigh Security, Axis Security, VMware, Inc., Fortinet, Inc., and others. These companies offer a wide range of SSE solutions, including cloud-based firewalls, secure web gateways, and cloud access security brokers. The increasing adoption of SSE solutions is being driven by the need for organizations to protect their data and applications in the cloud, as well as the need to comply with regulatory requirements.

The global Security Service Edge (SSE) market is projected to reach USD 730.2 million by 2033, exhibiting a CAGR of 2.5% during the forecast period (2025-2033). Rising demand for cloud-based security solutions, increasing sophistication of cyber threats, and growing regulatory compliance requirements are driving the market growth. The adoption of SSE services enables organizations to consolidate their security infrastructure and improve their overall security posture. The market is segmented based on application and type. By application, the IT sector holds a significant market share due to the high concentration of data and critical applications in this industry. The BFSI sector is also a major contributor to the market, as banks and financial institutions require robust security measures to protect sensitive financial information. Cloud-based SSE solutions are gaining popularity due to their scalability, cost-effectiveness, and ease of deployment. Key players in the market include Zscaler, Palo Alto Networks, Cisco, Cato Networks, Netskope, and Proofpoint, among others. These companies are investing heavily in research and development to enhance their offerings and gain a competitive edge.

The Security Service Edge (SSE) market is experiencing robust growth, driven by the increasing adoption of cloud-based applications and remote work models. The shift towards distributed workforces necessitates secure access to applications and data regardless of location, fueling demand for comprehensive security solutions like SSE. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $75 billion by 2033. This expansion is further accelerated by the growing sophistication of cyber threats and the need for improved visibility and control over network traffic. Key drivers include the rise of Software as a Service (SaaS) adoption, the increasing prevalence of remote work, and the growing need for improved security posture management in a distributed environment. The market's growth is also spurred by advancements in technologies such as zero trust network access (ZTNA) and Secure Access Service Edge (SASE), which are integral components of SSE. Leading vendors such as Zscaler, Palo Alto Networks, and Cisco are actively investing in research and development to enhance their SSE offerings. The competitive landscape is characterized by both established players and emerging startups, resulting in innovation and a broader range of solutions. However, the market faces challenges such as integration complexities and the need for robust security awareness training among end-users. Despite these restraints, the long-term outlook for the SSE market remains positive, fueled by continuous technological advancements and the increasing reliance on cloud and mobile technologies across various industries. Segmentation within the market includes solutions categorized by functionality (e.g., Secure Web Gateway, CASB, ZTNA), deployment model (cloud, on-premises, hybrid), and industry vertical (e.g., finance, healthcare, education). The North American region is expected to hold a significant market share, followed by Europe and Asia-Pacific, driven by early adoption of cloud technologies and stringent data privacy regulations.

Timeseries data from 'GA Watkinsville 5 SSE' (ncei-uscrn-ga_watkinsville_5_sse) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,air_temperature_cm_time_maximum_over_pt1h_qc_agg,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,solar_irradiance_cm_time_minimum_over_pt1h_min,soil_temperature_cm_time_mean_over_pt1h_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,solar_irradiance_cm_time_mean,soil_temperature_cm_time_mean_over_pt1h,soil_moisture_percent_qc_agg,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-83.3896 featureType=TimeSeriesProfile geospatial_lat_max=33.7837 geospatial_lat_min=33.7837 geospatial_lat_units=degrees_north geospatial_lon_max=-83.3896 geospatial_lon_min=-83.3896 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-1.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127025 infoUrl=https://sensors.ioos.us/#metadata/127025/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=33.7837 platform=fixed platform_name=GA Watkinsville 5 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1111,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1111 Southernmost_Northing=33.7837 standard_name_vocabulary=CF Standard Name Table v72 station_id=127025 time_coverage_end=2025-08-11T04:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=-83.3896

The Security Service Edge (SSE) software market is experiencing robust growth, driven by the increasing adoption of cloud-based applications and remote work models. The shift towards distributed workforces necessitates secure access to corporate resources regardless of location, fueling demand for comprehensive SSE solutions. Key drivers include the need to improve security posture against sophisticated cyber threats, enhance compliance with data privacy regulations like GDPR and CCPA, and streamline network security management. The market is segmented by deployment model (cloud, on-premises), component (secure web gateway, secure access service edge, CASB, zero trust network access), enterprise size (small, medium, large), and industry vertical (BFSI, healthcare, government). Competition is fierce, with established players like Zscaler, Netskope, and Palo Alto Networks vying for market share alongside emerging innovative companies. The market's growth trajectory is projected to remain positive, fueled by continuous technological advancements in areas such as AI-driven threat detection, automation, and improved user experience. A continued focus on integration with existing security infrastructure and a move toward unified security platforms will be crucial for vendors to thrive. The forecast period (2025-2033) anticipates sustained growth, albeit potentially at a slightly moderated CAGR compared to previous years. This moderation might reflect market maturity and increasing competition. However, the underlying demand for robust security solutions in the face of evolving cyber threats will ensure consistent market expansion. Factors such as increasing cybersecurity awareness, stricter regulations, and the expansion of digital transformation initiatives across various industries will continue to positively influence the SSE market's growth. The expansion into emerging markets and the integration of SSE with other security technologies, such as endpoint detection and response (EDR) and extended detection and response (XDR), will be pivotal in shaping the market landscape in the coming years. We project strong growth in North America and Europe, with Asia-Pacific exhibiting significant potential for future expansion.

Timeseries data from 'VA Charlottesville 2 SSE' (ncei-uscrn-va_charlottesville_2_) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,air_temperature_cm_time_maximum_over_pt1h_qc_agg,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,solar_irradiance_cm_time_minimum_over_pt1h_min,soil_temperature_cm_time_mean_over_pt1h_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,solar_irradiance_cm_time_mean,soil_temperature_cm_time_mean_over_pt1h,soil_moisture_percent_qc_agg,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-78.4656 featureType=TimeSeriesProfile geospatial_lat_max=37.9975 geospatial_lat_min=37.9975 geospatial_lat_units=degrees_north geospatial_lon_max=-78.4656 geospatial_lon_min=-78.4656 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-0.1 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127108 infoUrl=https://sensors.ioos.us/#metadata/127108/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=37.9975 platform=fixed platform_name=VA Charlottesville 2 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1346,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1346 Southernmost_Northing=37.9975 standard_name_vocabulary=CF Standard Name Table v72 station_id=127108 time_coverage_end=2025-08-05T05:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=-78.4656

Timeseries data from 'OH Wooster 3 SSE' (ncei-uscrn-oh_wooster_3_sse-v2) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,air_temperature_cm_time_maximum_over_pt1h_qc_agg,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,solar_irradiance_cm_time_minimum_over_pt1h_min,soil_temperature_cm_time_mean_over_pt1h_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,solar_irradiance_cm_time_mean,soil_temperature_cm_time_mean_over_pt1h,soil_moisture_percent_qc_agg,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-81.9104 featureType=TimeSeriesProfile geospatial_lat_max=40.7641 geospatial_lat_min=40.7641 geospatial_lat_units=degrees_north geospatial_lon_max=-81.9104 geospatial_lon_min=-81.9104 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-0.1 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127076 infoUrl=https://sensors.ioos.us/#metadata/127076/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=40.7641 platform=fixed platform_name=OH Wooster 3 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1797,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1797 Southernmost_Northing=40.7641 standard_name_vocabulary=CF Standard Name Table v72 station_id=127076 time_coverage_end=2025-08-01T04:00:00Z time_coverage_start=2016-11-10T22:00:00Z Westernmost_Easting=-81.9104

Parameters of the ASSET method employed for the analysis of stochastic and network data. Each column shows the parameters employed for the corresponding step of the method.

Timeseries data from 'FL Sebring 23 SSE (ncei-uscrn-fl_sebring_23_sse-v2)' (ism-secoora-ncei-uscrn-fl_sebrin) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com,feedback@axiomdatascience.com contributor_name=Axiom Data Science,Axiom Data Science contributor_role=contributor,processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=relative_humidity_qc_agg,air_temperature,solar_irradiance,soil_temperature,soil_temperature_qc_agg,surface_temperature_qc_agg,z,time,solar_irradiance_qc_agg,relative_humidity,surface_temperature,air_temperature_qc_agg&time>=max(time)-3days Easternmost_Easting=-81.3689 featureType=TimeSeriesProfile geospatial_lat_max=27.1526 geospatial_lat_min=27.1526 geospatial_lat_units=degrees_north geospatial_lon_max=-81.3689 geospatial_lon_min=-81.3689 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-0.5 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from Southeast Coastal Ocean Observing Regional Association (SECOORA) at https://erddap.secoora.org/erddap/tabledap/ncei-uscrn-fl_sebring_23_sse-v2 id=127747 infoUrl=https://sensors.ioos.us/#metadata/127747/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=27.1526 platform=fixed platform_name=FL Sebring 23 SSE (ncei-uscrn-fl_sebring_23_sse-v2) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://erddap.secoora.org/erddap/tabledap/ncei-uscrn-fl_sebring_23_sse-v2,https://erddap.secoora.org/erddap/tabledap/ncei-uscrn-fl_sebring_23_sse-v2, sourceUrl=https://erddap.secoora.org/erddap/tabledap/ncei-uscrn-fl_sebring_23_sse-v2 Southernmost_Northing=27.1526 standard_name_vocabulary=CF Standard Name Table v72 station_id=127747 time_coverage_end=2025-07-28T04:00:00Z time_coverage_start=2023-04-28T14:00:00Z Westernmost_Easting=-81.3689

The Secure Access Service Edge (SASE) market is experiencing robust growth, projected to reach a market size of $10.76 billion in 2025, with a compound annual growth rate (CAGR) of 20.29% from 2025 to 2033. This rapid expansion is driven by several key factors. The increasing adoption of cloud-based applications and remote work models necessitates secure and reliable access to corporate resources from anywhere, fueling the demand for SASE solutions. Furthermore, the rising prevalence of sophisticated cyber threats and the need for improved network security are compelling organizations to adopt SASE architectures that integrate network security functions like Secure Web Gateway (SWG), Cloud Access Security Broker (CASB), and Zero Trust Network Access (ZTNA) into a single, cloud-delivered platform. This simplifies management, enhances security posture, and improves operational efficiency. The market is witnessing significant innovation with the emergence of advanced features like AI-driven threat detection and automated security policy enforcement. Major players like Cisco, VMware, Palo Alto Networks, and Fortinet are actively investing in research and development, expanding their product portfolios, and forging strategic partnerships to gain a competitive edge. The market is segmented by deployment model (cloud, on-premises), organization size (small and medium-sized enterprises, large enterprises), and vertical (BFSI, healthcare, retail, etc.), each exhibiting unique growth trajectories. While the market presents significant opportunities, challenges remain, including integration complexities, security concerns related to cloud dependency, and the need for skilled professionals to manage and maintain SASE solutions. However, the overall outlook for the SASE market is extremely positive, with continuous expansion expected throughout the forecast period due to the enduring demand for secure and agile access solutions in the evolving digital landscape. Recent developments include: June 2024- Tata Communications, in collaboration with Versa Networks, one of the prominent providers of AI-driven Unified SASE solutions, has introduced its Unified/ Single-Vendor Hosted Secure Access Service Edge (SASE) designed for global enterprises. This solution integrates software-defined wide area networks (SD-WAN) with secure service edge (SSE) capabilities, utilizing single-pass technology., May 2024- iboss, a company in cloud security for heavily regulated sectors, has introduced its latest innovation: the Zero Trust SD-WAN solution. This launch positions iboss as a comprehensive Zero Trust SASE provider, seamlessly integrating its acclaimed Zero Trust SSE platform with the new SD-WAN offering. This unified solution enables organizations to establish secure connections across factories, branch offices, and data centers, eliminating the need for outdated firewalls and complex VPNs., August 2023 - Carlsberg Group selected Cato Networks for a massive global SASE deployment. Cato Networks announced Carlsberg Group as its latest enterprise customer. The third-largest brewer in the world chose a single-vendor SASE in order to transform its global network and security infrastructure. The Cato deployment spans over 200 locations and 25,000 remote users globally. Instead of security appliances, Carlsberg relies on Cato's cloud-native security capabilities, including SWG, CASB, DLP, ZTNA, FWaaS, IPS, and NGAM.. Key drivers for this market are: Growing Need for a Single Network Architecture that Combines SD-WAN, FWaaS, SWG, CASB, and ZTNA Capabilities, Lack of Security Procedures and Tools; Mandatory Compliance with Data Protection and Regulatory Legislation. Potential restraints include: Growing Need for a Single Network Architecture that Combines SD-WAN, FWaaS, SWG, CASB, and ZTNA Capabilities, Lack of Security Procedures and Tools; Mandatory Compliance with Data Protection and Regulatory Legislation. Notable trends are: Large Enterprises will Hold Major Market Shares.

Timeseries data from 'AL Selma 6 SSE' (urn:ioos:station:gov.noaa.uscrn:AL_Selma_6_SSE) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 defaultDataQuery=lwe_thickness_of_precipitation_amount_cm_time_sum_over_pt1h,air_temperature_cm_time_mean_over_pt1h,air_temperature_cm_time_maximum_over_pt1h,z,air_temperature_cm_time_minimum_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-86.98 featureType=TimeSeries geospatial_lat_max=32.33 geospatial_lat_min=32.33 geospatial_lat_units=degrees_north geospatial_lon_max=-86.98 geospatial_lon_min=-86.98 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=56266 infoUrl=https://sensors.ioos.us/#metadata/56266/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=32.33 platform=fixed platform_name=AL Selma 6 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=,, sourceUrl=https://sensors.axds.co/api/ Southernmost_Northing=32.33 standard_name_vocabulary=CF Standard Name Table v72 time_coverage_end=2023-01-21T04:00:00Z time_coverage_start=2015-08-23T14:00:00Z Westernmost_Easting=-86.98

Timeseries data from 'AL Russellville 4 SSE' (ncei-uscrn-al_russellville_4_sse) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,air_temperature_cm_time_maximum_over_pt1h_qc_agg,soil_temperature_cm_time_mean_over_pt1h,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,soil_moisture_percent_qc_agg,air_temperature_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,time,soil_temperature_cm_time_mean_over_pt1h_qc_agg&time>=max(time)-3days Easternmost_Easting=-87.7104 featureType=TimeSeriesProfile geospatial_lat_max=34.4535 geospatial_lat_min=34.4535 geospatial_lat_units=degrees_north geospatial_lon_max=-87.7104 geospatial_lon_min=-87.7104 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-1.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127128 infoUrl=https://sensors.ioos.us/#metadata/127128/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=34.4535 platform=fixed platform_name=AL Russellville 4 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1297,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1297 Southernmost_Northing=34.4535 standard_name_vocabulary=CF Standard Name Table v72 station_id=127128 time_coverage_end=2025-08-11T13:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=-87.7104

Data Source The data of SSE - Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

Timeseries data from 'SA Tiksi 4 SSE' (ncei-uscrn-sa_tiksi_4_sse-v2) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,air_temperature_cm_time_maximum_over_pt1h_qc_agg,solar_irradiance_cm_time_mean,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min,z,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=128.9158 featureType=TimeSeries geospatial_lat_max=71.5855 geospatial_lat_min=71.5855 geospatial_lat_units=degrees_north geospatial_lon_max=128.9158 geospatial_lon_min=128.9158 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127089 infoUrl=https://sensors.ioos.us/#metadata/127089/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=71.5855 platform=fixed platform_name=SA Tiksi 4 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1789,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1789 Southernmost_Northing=71.5855 standard_name_vocabulary=CF Standard Name Table v72 station_id=127089 time_coverage_end=2018-11-06T23:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=128.9158

Timeseries data from 'WY Lander 11 SSE' (ncei-uscrn-wy_lander_11_sse-v2) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,air_temperature_cm_time_maximum_over_pt1h_qc_agg,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,solar_irradiance_cm_time_minimum_over_pt1h_min,soil_temperature_cm_time_mean_over_pt1h_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,solar_irradiance_cm_time_mean,soil_temperature_cm_time_mean_over_pt1h,soil_moisture_percent_qc_agg,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-108.6686 featureType=TimeSeriesProfile geospatial_lat_max=42.6754 geospatial_lat_min=42.6754 geospatial_lat_units=degrees_north geospatial_lon_max=-108.6686 geospatial_lon_min=-108.6686 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-0.1 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127115 infoUrl=https://sensors.ioos.us/#metadata/127115/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=42.6754 platform=fixed platform_name=WY Lander 11 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1144,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1144 Southernmost_Northing=42.6754 standard_name_vocabulary=CF Standard Name Table v72 station_id=127115 time_coverage_end=2025-08-12T13:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=-108.6686

Timeseries data from 'AK Yakutat 3 SSE' (ncei-uscrn-ak_yakutat_3_sse-v2) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,air_temperature_cm_time_maximum_over_pt1h_qc_agg,solar_irradiance_cm_time_mean,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,air_temperature_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min,z,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-139.685 featureType=TimeSeries geospatial_lat_max=59.5087 geospatial_lat_min=59.5087 geospatial_lat_units=degrees_north geospatial_lon_max=-139.685 geospatial_lon_min=-139.685 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=126997 infoUrl=https://sensors.ioos.us/#metadata/126997/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=59.5087 platform=fixed platform_name=AK Yakutat 3 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1796,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1796 Southernmost_Northing=59.5087 standard_name_vocabulary=CF Standard Name Table v72 station_id=126997 time_coverage_end=2025-08-11T13:00:00Z time_coverage_start=2016-08-27T02:00:00Z Westernmost_Easting=-139.685

Timeseries data from 'NE Harrison 20 SSE' (ncei-uscrn-ne_harrison_20_sse-v2) cdm_altitude_proxy=z cdm_data_type=TimeSeriesProfile cdm_profile_variables=time cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=air_temperature_cm_time_minimum_over_pt1h_qc_agg,solar_irradiance_cm_time_minimum_over_pt1h_min_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h,solar_irradiance_cm_time_maximum_over_pt1h_max_qc_agg,air_temperature_cm_time_maximum_over_pt1h_qc_agg,surface_temperature_cm_time_mean_over_pt1h_qc_agg,air_temperature_cm_time_maximum_over_pt1h,relative_humidity_cm_time_mean_over_pt1h_mean_qc_agg,relative_humidity_cm_time_mean_over_pt1h_mean,solar_irradiance_cm_time_minimum_over_pt1h_min,soil_temperature_cm_time_mean_over_pt1h_qc_agg,solar_irradiance_cm_time_maximum_over_pt1h_max,solar_irradiance_cm_time_mean,soil_temperature_cm_time_mean_over_pt1h,soil_moisture_percent_qc_agg,air_temperature_qc_agg,air_temperature,air_temperature_cm_time_mean_over_pt1h_mean_qc_agg,solar_irradiance_cm_time_mean_qc_agg,lwe_precipitation_rate_cm_time_sum_over_pt1h_qc_agg,z,soil_moisture_percent,air_temperature_cm_time_minimum_over_pt1h,air_temperature_cm_time_mean_over_pt1h_mean,surface_temperature_cm_time_mean_over_pt1h,time&time>=max(time)-3days Easternmost_Easting=-103.7363 featureType=TimeSeriesProfile geospatial_lat_max=42.4247 geospatial_lat_min=42.4247 geospatial_lat_units=degrees_north geospatial_lon_max=-103.7363 geospatial_lon_min=-103.7363 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=-1.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from US Climate Research Network (USCRN, NOAA) at id=127062 infoUrl=https://sensors.ioos.us/#metadata/127062/station institution=US Climate Research Network (USCRN, NOAA) naming_authority=com.axiomdatascience Northernmost_Northing=42.4247 platform=fixed platform_name=NE Harrison 20 SSE platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1025,, sourceUrl=https://www.ncei.noaa.gov/access/crn/sensors.htm?stationId=1025 Southernmost_Northing=42.4247 standard_name_vocabulary=CF Standard Name Table v72 station_id=127062 time_coverage_end=2025-08-11T14:00:00Z time_coverage_start=2015-01-01T01:00:00Z Westernmost_Easting=-103.7363

Training and validation data per neural network.