Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific?
We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (<i>Apis mellifera</i>) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales a...
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2025-07-01
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| author | Vladimir A. Kulyukin Aleksey V. Kulyukin William G. Meikle |
| author_facet | Vladimir A. Kulyukin Aleksey V. Kulyukin William G. Meikle |
| author_sort | Vladimir A. Kulyukin |
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| description | We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (<i>Apis mellifera</i>) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does seasonality matter in the ARIMA forecasting of hive weight and in-hive temperature? (b) To what extent do the best forecasters of one hive generalize to other hives? and (c) Which time series type (i.e., hive weight or in-hive temperature) is better predictable? Our principal findings were as follows: (1) The hive weight and in-hive temperature series were not white noise, were not normally distributed, and, for most hives, were not difference- or trend-stationary; (2) Seasonality matters, in that seasonal ARIMA (SARIMA) forecasters outperformed their ARIMA counterparts on the curated dataset; (3) The best hive weight and in-hive temperature forecasters of the ten monitored colonies appeared to be colony-specific; (4) The accuracy of the hive weight forecasts was consistently higher than that of the in-hive temperature forecasts; (5) The weight and temperature forecasts exhibited common qualitative patterns. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-955df9e99bde49458cde5ca6d50160282025-08-20T03:32:33ZengMDPI AGSensors1424-82202025-07-012514431910.3390/s25144319Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific?Vladimir A. Kulyukin0Aleksey V. Kulyukin1William G. Meikle2Department of Computer Science, Utah State University, Logan, UT 84322, USADepartment of Mathematics and Statistics, Utah State University, Logan, UT 84322, USACarl Hayden Bee Research Center, USDA-ARS, Tucson, AZ 85719, USAWe explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (<i>Apis mellifera</i>) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does seasonality matter in the ARIMA forecasting of hive weight and in-hive temperature? (b) To what extent do the best forecasters of one hive generalize to other hives? and (c) Which time series type (i.e., hive weight or in-hive temperature) is better predictable? Our principal findings were as follows: (1) The hive weight and in-hive temperature series were not white noise, were not normally distributed, and, for most hives, were not difference- or trend-stationary; (2) Seasonality matters, in that seasonal ARIMA (SARIMA) forecasters outperformed their ARIMA counterparts on the curated dataset; (3) The best hive weight and in-hive temperature forecasters of the ten monitored colonies appeared to be colony-specific; (4) The accuracy of the hive weight forecasts was consistently higher than that of the in-hive temperature forecasts; (5) The weight and temperature forecasts exhibited common qualitative patterns.https://www.mdpi.com/1424-8220/25/14/4319precision apiculturecontinuous beehive monitoringelectronic beehive monitoringsensor-based hive monitoringtime series forecastingARIMA |
| spellingShingle | Vladimir A. Kulyukin Aleksey V. Kulyukin William G. Meikle Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? Sensors precision apiculture continuous beehive monitoring electronic beehive monitoring sensor-based hive monitoring time series forecasting ARIMA |
| title | Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? |
| title_full | Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? |
| title_fullStr | Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? |
| title_full_unstemmed | Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? |
| title_short | Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? |
| title_sort | discrete time series forecasting in non invasive monitoring of managed honey bee colonies part ii are hive weight and in hive temperature seasonal and colony specific |
| topic | precision apiculture continuous beehive monitoring electronic beehive monitoring sensor-based hive monitoring time series forecasting ARIMA |
| url | https://www.mdpi.com/1424-8220/25/14/4319 |
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