Timeseries Downsampling for Vehicle Data Market Outlook

According to our latest research, the market size of the global Timeseries Downsampling for Vehicle Data Market in 2024 stands at USD 1.34 billion, reflecting the rapid integration of advanced data management solutions within the automotive sector. The market is experiencing robust growth, with a projected CAGR of 18.7% from 2025 to 2033. By 2033, the market is anticipated to reach USD 6.13 billion, underscoring the increasing demand for efficient data processing techniques to manage the exponential rise in vehicle-generated data. A key driver behind this growth is the automotive industry's shift towards connected and autonomous vehicles, necessitating more scalable and efficient data handling methodologies.

The primary growth factor for the Timeseries Downsampling for Vehicle Data Market is the escalating volume of data generated by modern vehicles, including sensor, GPS, and telemetry data. As vehicles become more connected and equipped with advanced driver assistance systems (ADAS), the sheer volume of raw data overwhelms traditional storage and analytics infrastructures. Downsampling techniques provide a practical solution by reducing data granularity while retaining essential features, enabling real-time analytics and long-term data storage. This capability is crucial for applications such as predictive maintenance, fleet management, and autonomous driving, where timely and accurate data insights are paramount. The ongoing digital transformation in the automotive sector, coupled with the proliferation of IoT devices, further amplifies the need for sophisticated downsampling solutions.

Another significant driver is the increasing adoption of cloud-based platforms and big data analytics in automotive operations. As OEMs and fleet operators strive to harness actionable insights from vast datasets, the need for efficient data compression and storage becomes evident. Timeseries downsampling not only reduces the computational burden but also optimizes bandwidth utilization and lowers operational costs. This is particularly relevant for global fleet operators who manage vehicles across diverse geographies and require scalable solutions to aggregate, process, and analyze data remotely. The integration of AI and machine learning algorithms with downsampled data further enhances predictive analytics, enabling proactive maintenance and improved vehicle performance, thus fueling market expansion.

Regulatory pressures and the growing emphasis on data privacy and security also contribute to the market's upward trajectory. Stringent regulations regarding vehicle data management, especially in regions such as Europe and North America, compel automotive stakeholders to adopt best-in-class data processing practices. Timeseries downsampling enables compliance by minimizing data retention risks and ensuring only the most relevant information is stored and transmitted. Moreover, the rise of shared mobility and telematics-based insurance models has intensified the need for efficient data handling, as insurers and service providers increasingly rely on downsampled data to assess driver behavior and vehicle usage patterns. Collectively, these factors position timeseries downsampling as a foundational technology in the evolving landscape of vehicle data management.

From a regional perspective, North America leads the Timeseries Downsampling for Vehicle Data Market, driven by the presence of major automotive OEMs, technology innovators, and a mature regulatory framework. Europe follows closely, benefiting from strong R&D investments and progressive data governance policies. Asia Pacific is emerging as a high-growth region, propelled by rapid vehicle electrification, smart city initiatives, and the expansion of connected vehicle ecosystems in countries like China, Japan, and South Korea. Latin America and the Middle East & Africa are gradually adopting advanced vehicle data solutions, albeit at a slower pace, due to infrastructure and regulatory constraints. Nonetheless, the global outlook remains highly positive, with all regions expected to contribute to the market's sustained expansion through 2033.

Technique Analysis

The Timeseries Downsampling for Vehicle Data Market is segmented by technique into Uniform Sampling, Adaptive Sampling, Event-Based Sampling, and Others. Uniform Sampling remains a foundational approach, offering s

Additional file 1:. Machine Learning Functions. Code used to apply the machine learning algorithms

Time‑Series Downsampling as a Service Market Outlook

According to our latest research, the global time-series downsampling as a service market size reached USD 1.36 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 19.7% during the forecast period, reaching a forecasted value of USD 6.08 billion by 2033. This impressive growth is primarily driven by the exponential increase in time-series data generated by IoT devices, digital transformation initiatives across industries, and the need for efficient data storage and real-time analytics solutions.

A significant growth factor for the time-series downsampling as a service market is the rapid proliferation of IoT and sensor-based applications across sectors such as manufacturing, healthcare, smart cities, and energy management. As organizations deploy millions of sensors and edge devices, the volume of time-series data being generated is skyrocketing. This data, if stored and analyzed at its raw granularity, can overwhelm storage infrastructure and analytics platforms. Time-series downsampling services offer a critical solution by reducing data volume while retaining essential trends and patterns, enabling organizations to manage data more cost-effectively and extract actionable insights in real time. The growing complexity and scale of IoT deployments are thus directly fueling the demand for advanced downsampling services.

Another key driver is the shift toward cloud-based analytics and data management platforms. Enterprises are increasingly adopting cloud-native architectures to leverage the scalability, flexibility, and cost advantages offered by cloud service providers. Time-series downsampling as a service integrates seamlessly with these cloud environments, allowing organizations to efficiently process, store, and analyze vast amounts of time-stamped data without significant investments in on-premises infrastructure. The growing adoption of AI and machine learning for predictive analytics also necessitates efficient data preprocessing, further boosting the adoption of downsampling services to ensure that models are trained on high-quality, representative datasets.

In addition, regulatory compliance and data governance requirements are playing a pivotal role in shaping the market landscape. Industries such as banking, financial services, insurance (BFSI), and healthcare are subject to strict regulations regarding data retention, privacy, and auditability. Time-series downsampling enables these organizations to balance compliance needs with operational efficiency by retaining only the most critical data points over time. This capability not only helps in reducing storage costs but also ensures that historical data remains accessible and manageable for audits and compliance reporting. The interplay between regulatory mandates and operational imperatives is expected to sustain strong demand for downsampling solutions in the coming years.

Regionally, North America currently dominates the time-series downsampling as a service market, accounting for over 38% of global revenue in 2024. This leadership position is attributed to the early adoption of digital transformation initiatives, a mature cloud ecosystem, and the presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 22.1% from 2025 to 2033, driven by rapid industrialization, expanding IoT deployments, and increasing investments in smart infrastructure across China, India, and Southeast Asia. Europe also represents a significant market, underpinned by strong industrial automation trends and stringent data protection regulations.

Component Analysis

The component segment of the time-series downsampling as a service market is bifurcated into software and services. The software segment encompasses specialized platforms and tools designed to automate and optimi

This dataset presents the data for the paper titled: Prediction of vegetation indices from down-sampled hyperspectral data using machine learning: A novel framework for olive crop monitoring. The dataset presents files containing the hyperspectral information from olive leaves in the range from 350 to 2500 nm, and the downsampling information from files at a rate of 5, 10, 20, 30, 40, 50, 75 and 100 nm, and the 25 vegetation indices analyzed in this study. The files are saved as .mat files and can be used in Matlab.This dataset also contains the .arff files at each drying stage, that can be used to train models in the WEKA software.Finally we include a brief file, describing how to use the WEKA software.

Time‑Series Downsampling as a Service Market Outlook

According to our latest research, the global Time-Series Downsampling as a Service market size reached USD 1.42 billion in 2024, driven by the exponential growth in data generation across industries. The market is expected to expand at a robust CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to hit USD 7.52 billion by 2033. This impressive growth is primarily fueled by the rising need for efficient data storage, real-time analytics, and scalable cloud-based solutions that can manage and optimize massive time-series datasets across various verticals.

One of the core growth factors propelling the Time-Series Downsampling as a Service market is the surging volume of data generated by IoT devices, sensors, and digital platforms. As organizations increasingly deploy smart sensors in manufacturing, energy, healthcare, and logistics, the resulting flood of time-stamped data overwhelms traditional storage and analytics systems. Downsampling as a service enables enterprises to reduce data granularity and storage costs, while maintaining the integrity and usability of critical information for analytics. This is especially vital for industries like financial services and network monitoring, where high-frequency data streams require efficient compression without loss of key insights. The ability to automate and scale downsampling processes through service-based models is becoming indispensable for digital transformation initiatives worldwide.

Another significant driver is the growing adoption of cloud-based infrastructure and analytics platforms. Organizations are moving away from on-premises solutions to embrace cloud-native architectures that offer scalability, flexibility, and cost-effectiveness. Time-Series Downsampling as a Service leverages cloud capabilities to deliver real-time, on-demand data reduction and transformation, supporting advanced analytics, machine learning, and visualization. This shift is particularly evident in sectors such as IT & telecommunications, BFSI, and healthcare, where agility and rapid access to actionable insights are paramount. The proliferation of SaaS-based analytics tools and the integration of AI-driven data management further accelerate market adoption, as companies seek to optimize their data pipelines for performance and compliance.

Additionally, regulatory requirements and data governance standards are shaping the market landscape. Organizations must comply with data retention, privacy, and security mandates, which often necessitate the efficient management of large volumes of time-series data. Downsampling as a service helps enterprises strike a balance between regulatory compliance and operational efficiency by enabling selective retention, anonymization, and aggregation of sensitive data. This capability is especially relevant for highly regulated industries such as banking, healthcare, and energy, where non-compliance can result in severe penalties. As data regulations evolve and become more stringent, the demand for robust, compliant downsampling services is expected to surge.

From a regional perspective, North America currently leads the Time-Series Downsampling as a Service market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the early adoption of IoT, advanced analytics, and cloud computing across key industries. However, Asia Pacific is anticipated to witness the highest growth rate over the forecast period, driven by rapid digitalization, increasing investments in smart infrastructure, and expanding industrial automation in countries like China, India, and Japan. Europe remains a significant market, characterized by stringent data governance policies and a strong emphasis on technological innovation in manufacturing and energy sectors. The Middle East & Africa and Latin America are also emerging as promising markets, supported by ongoing digital transformation initiatives and the proliferation of connected devices.

Component Analysis

The Time-Series Downsampling as a Service market is segmented by component into Software and Services. The software segment dominates the market, accounting for a substantial portion of the total revenue in 2024. This dominance is primarily due to the widespread adoption of advanced downsampling algorithms, data transformation engines, and integration capabilities offered by leadin

Remote sensing object detection (RSOD) is highly challenging due to large variations in object scales. Existing deep learning-based methods still face limitations in addressing this challenge. Specifically, reliance on stride convolutions during downsampling leads to the loss of object information, and insufficient context-aware modeling capability hampers full utilization of object information at different scales. To address these issues, this paper proposes a Haar wavelet-based Attention Network (HWANet). The model includes a Low-frequency Enhanced Downsampling Module (LEM), a Haar Frequency Domain Self-attention Module (HFDSA), and a Spatial Information Interaction Module (SIIM). Specifically, LEM employs the Haar wavelet transform to downsample feature maps and enhances low-frequency components, mitigating the loss of object information at different scales. The HFDSA module integrates Haar wavelet transform and explicit spatial priors, reducing computational complexity while enhancing the capture of image spatial structures. Meanwhile, the SIIM module facilitates interactions among information at different levels, enabling multi-level feature integration. Together, SIIM and HFDSA strengthen the model’s context-aware modeling capability, allowing full utilization of multi-scale information. Experimental results show that HWANet achieves 93.1% mAP50 on the NWPU VHR-10 dataset and 99.1% mAP50 on the SAR-Airport-1.0 dataset, with only 2.75M parameters, outperforming existing methods.

FallAllD/Derived FallAllD Dataset - Fall Detection

1. Original FallAllD.pkl by Majd SALEH

Description: FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.

FallAllD is described in detail in the following journal article:

M. Saleh, M. Abbas and R. L. B. Jeannès, "FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3018335.

Attributes:

• Sensors: Accelerometer (Acc), Gyroscope (Gyr), Magnetometer (Mag), Barometer (Bar) • Device Positions: Waist, Wrist, Neck • Sampling Rate: 238 Hz/80 Hz/10 Hz • Activities: Multiple types of falls and various ADLs • Data Format: Pickle file containing raw sensor data with corresponding activity labels

2. activity_info.pkl

Description: The activity_info.pkl file is a derived dataset that maps activity IDs to their corresponding descriptions. It serves as a reference to understand the different activities included in the FallAllD dataset and the new derived dataset.

Attributes:

• ActivityID: Unique identifier for each activity • Description: Text description of the activity

Purpose:

• To provide a clear understanding of the activities represented by the ActivityIDs in the datasets. • Facilitates interpretation of activity labels during data analysis and model evaluation.

3. FallAllD_40SamplesPerSec_ActivityIdsFiltered.pkl

Description: The FallAllD_40SamplesPerSec_ActivityIdsFiltered.pkl is a processed and refined version of the original FallAllD dataset. It has been modified to focus on specific sensor data and activity types, making it more suitable for multi-class fall detection using machine learning.

Modifications:

• Downsampling to 40Hz: The original data, sampled at 238Hz, was downsampled to 40Hz to reduce the dataset size and computational requirements. • Removing 'Mag' and 'Bar' Sensor Data: Magnetometer (Mag) and Barometer (Bar) sensor data were removed to simplify the dataset, focusing only on accelerometer (Acc) and gyroscope (Gyr) data. • Removing Unnecessary 'ActivityIDs': Activities that were not relevant to the study or had insufficient data were removed to streamline the dataset. • Balancing the Classes with SMOTE: Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance the classes, addressing the issue of imbalanced data and ensuring a more robust model training process. • Removing 'Neck' Device Data: Data from the neck device was removed to focus on sensor data from the waist device, which was deemed more relevant for this study.

Attributes:

• Sensors: Accelerometer (Acc), Gyroscope (Gyr) • Device Position: Waist, Wrist • Sampling Rate: 40 samples per second • Activities: Filtered set of fall types and ADLs, represented by a refined list of ActivityIDs • Data Format: Pickle file containing processed sensor data with corresponding activity labels

The Original Dataset was taken from IEEE Paper, "FallAllD: A Comprehensive Dataset of Human Falls and Activities of Daily Living". M. Saleh, M. Abbas and R. L. B. Jeannès, "FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3018335.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and environmental challenges, including chemotherapy, cause a cell state transition, which is accompanied by a continuous morphological alteration that is often extremely difficult to recognize even by direct microscopic inspection. To determine whether deep learning-based image analysis enables the detection of cell shape reflecting a crucial cell state alteration, we used the oral cancer cell line resistant to chemotherapy but having cell morphology nearly indiscernible from its non-resistant parental cells. We then implemented the automatic approach via deep learning methods based on EfficienNet-B3 models, along with over- and down-sampling techniques to determine whether image analysis of the Convolutional Neural Network (CNN) can accomplish three-class classification of non-cancer cells vs. cancer cells with and without chemoresistance. We also examine the capability of CNN-based image analysis to approximate the composition of chemoresistant cancer cells within a population. We show that the classification model achieves at least 98.33% accuracy by the CNN model trained with over- and down-sampling techniques. For heterogeneous populations, the best model can approximate the true proportions of non-chemoresistant and chemoresistant cancer cells with Root Mean Square Error (RMSE) reduced to 0.16 by Ensemble Learning (EL). In conclusion, our study demonstrates the potential of CNN models to identify altered cell shapes that are visually challenging to recognize, thus supporting future applications with this automatic approach to image analysis.

Comparison Experiments for HWANet in NWPU VHR-10 Dataset.

The dataset was created specifically for analyzing mental fatigue based on physiological signals. It includes the following signals:

EEG (Electroencephalography): Measures the electrical activity of the brain. BVP (Blood Volume Pulse): Captures changes in blood volume in peripheral blood vessels. EDA (Electrodermal Activity): Tracks changes in the electrical conductance of the skin. HR (Heart Rate): Records the number of heartbeats per minute. TEMP (Temperature): Monitors the body temperature of the participants. ACC (Acceleration): Measures the acceleration experienced by the participants. The measurements were collected from 23 participants in both the morning and evening sessions. To evaluate the participants' mental fatigue levels, the Chalder Fatigue Scale was utilized. This scale assigns a score to each participant based on their responses, with a score of 12 or higher indicating a positive mental fatigue condition.

The dataset includes both raw and processed data. Raw data refers to the original recorded signals, while processed data typically involves signal preprocessing techniques such as filtering, artifact removal, and feature extraction.The processed data includes processed data based on different sampling frequencies (1 Hz, 32 Hz, and 64 Hz). These operations include downsampling, midsampling, and upsampling. The processed datasets are named MEFAR_DOWN, MEFAR_MID, and MEFAR_UP, corresponding to the processed data with downsampling, midsampling, and upsampling, respectively.This way, it was possible to analyze and compare data with different sampling frequencies.

The dataset has been used for training deep learning and transfer learning models, which suggests that it may be suitable for developing machine learning algorithms for mental fatigue detection based on physiological signals.

Additionally, demographic information about the participants is available in an Excel file called "general_info." It is important to ensure that the participants' anonymity and privacy are maintained in accordance with ethical guidelines.

By sharing this dataset, researchers interested in mental fatigue analysis can utilize it for further investigations, algorithm development, and validation.

Comparing the GPU Memory Usage of SIIM+HFDSA and SIIM+HFDSA (Without Using Haar Wavelet Transform).

Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by support vector machine with SMOTE-ENN (AUC = .942; balanced accuracy = .868, sensitivity = .898; specificity = .837). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Ablation Experiment on NWPU-VHR-10 without the distance decay matrix.

Comparison Experiments for HWANet in SAR-Airport-1.0.

Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global features but struggling with large-scale local features due to insufficient network depth. This limitation is particularly detrimental for segmenting diminutive targets such as brain tumors, where local feature extraction is crucial. Augmenting network depth to address this issue leads to excessive parameter counts, incompatible with resource-constrained devices. To tackle this challenge, we propose that object recognition should exhibit scale invariance, so we introduce a shared CNN network architecture for image encoding. The input MRI image is directly downsampled into three scales, with a shared 10-layer convolutional network employed across all scales to extract features. This approach enhances the network’s ability to capture large-scale local features without increasing the total parameter count. Further, we utilize a Transformer on the smallest scale to extract global features. The decoding stage follows the UNet structure, incorporating incremental upsampling and feature fusion from previous scales. Comparative experiments on the LGG Segmentation Dataset and BraTS21 dataset demonstrate that our proposed LiteMRINet achieves higher segmentation accuracy while significantly reducing parameter count. This makes our approach particularly advantageous for devices with limited memory resources. Our code is available at https://github.com/chinaericy/MRINet.

Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global features but struggling with large-scale local features due to insufficient network depth. This limitation is particularly detrimental for segmenting diminutive targets such as brain tumors, where local feature extraction is crucial. Augmenting network depth to address this issue leads to excessive parameter counts, incompatible with resource-constrained devices. To tackle this challenge, we propose that object recognition should exhibit scale invariance, so we introduce a shared CNN network architecture for image encoding. The input MRI image is directly downsampled into three scales, with a shared 10-layer convolutional network employed across all scales to extract features. This approach enhances the network’s ability to capture large-scale local features without increasing the total parameter count. Further, we utilize a Transformer on the smallest scale to extract global features. The decoding stage follows the UNet structure, incorporating incremental upsampling and feature fusion from previous scales. Comparative experiments on the LGG Segmentation Dataset and BraTS21 dataset demonstrate that our proposed LiteMRINet achieves higher segmentation accuracy while significantly reducing parameter count. This makes our approach particularly advantageous for devices with limited memory resources. Our code is available at https://github.com/chinaericy/MRINet.

Results of classic semantic segmentation on the test dataset.

Odds ratio for participant demographics characteristics (Odds Ratio Estimate, P-Value & 95% Confidence Interval).

Comparison of random prediction simulation, logistic regression, random forest, xgboost, balanced random forest, and balanced XGBoost models.

Odds ratio for evaluation measures (Odds Ratio Estimate, P-Value & 95% Confidence Interval).