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Analysis of time-series data allows to identify long-term trends and makepredictions that can help to improve our lives. With the rapid development ofartificial neural networks, long short-term memory (LSTM) recurrent neuralnetwork (RNN) configuration is found to be capable in dealing with time-seriesforecasting problems where data points are time-dependent and possessseasonality trends. Gated structure of LSTM cell and flexibility in networktopology (one-to-many, many-to-one, etc.) allows to model systems with multipleinput variables and control several parameters such as the size of thelook-back window to make a prediction and number of time steps to be predicted.These make LSTM attractive tool over conventional methods such asautoregression models, the simple average, moving average, naive approach,ARIMA, Holt's linear trend method, Holt's Winter seasonal method, and others.In this paper, we propose a hardware implementation of LSTM networkarchitecture for time-series forecasting problem. All simulations wereperformed using TSMC 0.18um CMOS technology and HP memristor model.

Memristive LSTM network hardware architecture for time-series predictive modeling problem